@@ -5,7 +5,7 @@ services: cognitive-services
 manager: nitinme
 ms.service: azure-ai-openai
 ms.topic: conceptual 
-ms.date: 11/01/2024
+ms.date: 01/08/2024
 author: mrbullwinkle
 ms.author: mbullwin
 recommendations: false
@@ -24,11 +24,13 @@ This article is to help you understand the support lifecycle for the Azure OpenA
 
 Azure OpenAI API latest release:
 
-- Inference: [2024-10-01-preview](https://github.com/Azure/azure-rest-api-specs/blob/main/specification/cognitiveservices/data-plane/AzureOpenAI/inference/preview/2024-10-01-preview/inference.json)
-- Authoring: [2024-10-01-preview](https://github.com/Azure/azure-rest-api-specs/blob/main/specification/cognitiveservices/data-plane/AzureOpenAI/authoring/preview/2024-10-01-preview/azureopenai.json)
+- Inference: [2024-12-01-preview](reference-preview.md)
+- Authoring: [2024-12-01-preview](https://github.com/Azure/azure-rest-api-specs/blob/main/specification/cognitiveservices/data-plane/AzureOpenAI/authoring/preview/2024-10-01-preview/azureopenai.json)
 
 This version contains support for the latest Azure OpenAI features including:
 
+- [Reasoning models](./how-to/reasoning.md) [**Added in 2024-12-01-preview**]
+- [Stored completions/distillation](./how-to/stored-completions.md) [**Added in 2024-12-01-preview**]
 - Assistants V2 [**Added in 2024-05-01-preview**]
 - Embeddings `encoding_format` and `dimensions` parameters [**Added in 2024-03-01-preview**]
 - [Assistants API](./assistants-reference.md). [**Added in 2024-02-15-preview**]
@@ -39,6 +41,12 @@ This version contains support for the latest Azure OpenAI features including:
 - [Function calling](./how-to/function-calling.md)  [**Added in 2023-07-01-preview**]
 - [Retrieval augmented generation with your data feature](./use-your-data-quickstart.md).  [**Added in 2023-06-01-preview**]
 
+## Changes between 2024-12-01-preview and 2024-10-01-preview
+
+- `store`, and `metadata` parameters added for [stored completions support](./how-to/stored-completions.md).
+- `reasoning_effort` added for latest [reasoning models](./how-to/reasoning.md).
+- `user_security_context` added for [Microsoft Defender for Cloud integration](https://aka.ms/TP4AI/Documentation/EndUserContext).
+
 ## Changes between 2024-09-01-preview and 2024-08-01-preview
 
 - `max_completion_tokens` added to support `o1-preview` and `o1-mini` models. `max_tokens` does not work with the **o1 series** models.

@@ -7,9 +7,9 @@ ms.service: azure-ai-openai
 ms.custom:
   - ignite-2024
 ms.topic: how-to
-ms.date: 11/11/2024
-author: mrbullwinkle
-ms.author: mbullwin
+ms.date: 01/08/2025
+author: aahill
+ms.author: aahi
 recommendations: false
 ---
 
@@ -51,11 +51,11 @@ Provisioned quota granularity is changing from model-specific to model-independe
 
 ## Model-independent quota
 
-Starting on August 12, 2024, existing customers' current, model-specific quota has been converted to model-independent. This happens automatically. No quota is lost in the transition. Existing quota limits are summed and assigned to a new model-independent quota item.
+As of August 12, 2024, existing customers' current, model-specific quota has been converted to model-independent. This happens automatically. No quota is lost in the transition. Existing quota limits are summed and assigned to a new model-independent quota item.
 
 :::image type="content" source="../media/provisioned/consolidation.png" alt-text="Diagram showing quota consolidation." lightbox="../media/provisioned/consolidation.png":::
 
-The new model-independent quota shows up as a quota item named **Provisioned Managed Throughput Unit**, with model and version no longer included in the name. In the Studio Quota pane, expanding the quota item still shows all of the deployments that contribute to the quota item. 
+The new model-independent quota shows up as a quota item named **Provisioned Managed Throughput Unit**, with model and version no longer included in the name. In the Azure AI Foundry's quota pane, expanding the quota item still shows all of the deployments that contribute to the quota item. 
 
 ### Default quota
 
@@ -67,15 +67,15 @@ For existing customers, if the region already contains a quota assignment, the q
 
 Customers no longer obtain quota by contacting their sales teams. Instead, they use the self-service quota request form and specify the PTU-Managed quota type. The form is accessible from a link to the right of the quota item. The target is to respond to all quota requests within two business days.  
 
-The following quota screenshot shows model-independent quota being used by deployments of different types, as well as the link for requesting additional quota. 
+The following quota screenshot shows model-independent quota being used by deployments of different types, and the link for requesting additional quota. 
 
 :::image type="content" source="../media/provisioned/quota-request-type.png" alt-text="Screenshot of new request type UI for Azure OpenAI provisioned for requesting more quota." lightbox="../media/provisioned/quota-request-type.png":::
 
 ## Quota as a limit
 
-Prior to the August update, Azure OpenAI Provisioned was only available to a few customers, and quota was allocated to maximize the ability for them to deploy and use it. With these changes, the process of acquiring quota is simplified for all users, and there is a greater likelihood of running into service capacity limitations when deployments are attempted. A new API and Studio experience are available to help users find regions where the subscription has quota and the service has capacity to support deployments of a desired model.
+Prior to the August update, Azure OpenAI Provisioned was only available to a few customers, and quota was allocated to maximize the ability for them to deploy and use it. With these changes, the process of acquiring quota is simplified for all users, and there is a greater likelihood of running into service capacity limitations when deployments are attempted. A new API and portal experience are available to help users find regions where the subscription has quota and the service has capacity to support deployments of a desired model.
 
-We also recommend that customers using commitments now create their deployments before creating or expanding commitments to cover them. This guarantees that capacity is available before creating a commitment and prevents over-purchase of the commitment. To support this, the restriction that prevented deployments from being created larger than their commitments has been removed. This new approach to quota, capacity availability and commitments matches what is provided under the hourly/reservation model, and the guidance to deploy before purchasing a commitment (or reservation, for the hourly model) is the same for both.
+We also recommend that customers using commitments now create their deployments before creating or expanding commitments to cover them. This guarantees that capacity is available before creating a commitment and prevents over-purchase of the commitment. To support this, the restriction that prevented deployments from being created larger than their commitments has been removed. This new approach to quota, capacity availability, and commitments matches what is provided under the hourly/reservation model, and the guidance to deploy before purchasing a commitment (or reservation, for the hourly model) is the same for both.
 
 See the following links for more information. The guidance for reservations and commitments is the same:
 
@@ -85,15 +85,15 @@ See the following links for more information. The guidance for reservations and
 ## New hourly reservation payment model
 
 > [!NOTE]
-> The following description of payment models does not apply to the older "Provisioned Classic (PTU-C)" offering. They only affect the Provisioned (aka Provisioned Managed) offering. Provisioned Classic continues to be governed by the unchanged monthly commitment payment model.
+> The following description of payment models doesn't apply to the older "Provisioned Classic (PTU-C)" offering. They only affect the Provisioned (also known as Provisioned Managed) offering. Provisioned Classic continues to be governed by the unchanged monthly commitment payment model.
 
 Microsoft has introduced a new "Hourly/reservation" payment model for provisioned deployments. This is in addition to the current **Commitment** payment model, which will continue to be supported at least through the end of 2024.
 
 ### Commitment payment model
 
 - A regional, monthly commitment is required to use provisioned (longer terms available contractually).
 
-- Commitments are bound to Azure OpenAI resources, which makes moving deployments across resources difficult.
+- Commitments are bound to Azure OpenAI resources, which will make moving deployments across resources difficult.
 
 - Commitments can't be canceled or altered during their term, except to add new PTUs.
 
@@ -149,9 +149,9 @@ Steps 1 and 2 are the same in all cases. The difference is whether a commitment
 * The best practice is to create deployments first, and then to apply discounts. This is to guarantee that service. capacity is available to support your deployments prior to creating a term commitment for PTUs you cannot use. 
 
 > [!NOTE] 
-> When you follow best practices, you may receive hourly charges between the time you create the deployment and increase your discount (commitment or reservation).   
+> When you follow best practices, you might receive hourly charges between the time you create the deployment and increase your discount (commitment or reservation).   
 >
-> For this reason, we recommend that you be prepared to increase your discount immediately following the deployment. The prerequisites for purchasing an Azure reservations are different than for commitments, and we recommend you validate them prior to deployment if you intend to use them to discount your deployment.  For more information, see [Permissions to view and manage Azure reservations](/azure/cost-management-billing/reservations/view-reservations) 
+> For this reason, we recommend that you be prepared to increase your discount immediately following the deployment. The prerequisites for purchasing an Azure reservations are different than for commitments, and we recommend you validate them prior to deployment if you intend to use them to discount your deployment. For more information, see [Permissions to view and manage Azure reservations](/azure/cost-management-billing/reservations/view-reservations) 
 
 ## Mapping deployments to discounting method 
 
@@ -206,7 +206,7 @@ An alternative approach to self-service migration is to switch the reservation p
 * There will be a short period of double-billing or hourly charges during the switchover from committed to hourly/reservation billing.
 
 > [!IMPORTANT]
-> Both self-service approaches generate some additional charges as the payment mode is switched from Committed to Hourly/Reservation.  These are characteristics of the migration approaches and customers aren't credited for these charges.  Customers may choose to use the managed migration approach described below to avoid them.
+> Both self-service approaches generate some additional charges as the payment mode is switched from Committed to Hourly/Reservation. These are characteristics of the migration approaches and customers aren't credited for these charges.  Customers can choose to use the managed migration approach described below to avoid them.
 
 ### Managed migration
 
@@ -233,7 +233,7 @@ Customers must reach out to their account teams to schedule a managed migration.
 
 ## Managing Provisioned Throughput Commitments
 
-Provisioned throughput commitments are created and managed from the **Manage Commitments** menu in Azure OpenAI Studio. You can navigate to this view by selecting **Manage Commitments** from the Quota menu:
+Provisioned throughput commitments are created and managed by selecting **Management center** in the Azure AI Foundry portal's navigation menu > **Quota** > **Manage Commitments**. 
 
 :::image type="content" source="../media/how-to/provisioned-onboarding/notifications.png" alt-text="Screenshot of commitment purchase UI with notifications." lightbox="../media/how-to/provisioned-onboarding/notifications.png":::
 
@@ -247,7 +247,7 @@ The following sections will take you through these tasks.
 
 ## Purchase a Provisioned Throughput Commitment
 
-With your commitment plan ready, the next step is to create the commitments. Commitments are created manually via Azure OpenAI Studio and require the user creating the commitment to have either the [Contributor or Cognitive Services Contributor role](../how-to/role-based-access-control.md) at the subscription level.
+With your commitment plan ready, the next step is to create the commitments. Commitments are created manually via the Azure AI Foundry and require the user creating the commitment to have either the [Contributor or Cognitive Services Contributor role](../how-to/role-based-access-control.md) at the subscription level.
 
 For each new commitment you need to create, follow these steps:
 
@@ -273,11 +273,11 @@ For each new commitment you need to create, follow these steps:
 :::image type="content" source="../media/how-to/provisioned-onboarding/commitment-tier.png" alt-text="Screenshot of commitment purchase UI." lightbox="../media/how-to/provisioned-onboarding/commitment-tier.png":::
 
 > [!IMPORTANT]
-> A new commitment is billed up-front for the entire term.  If the renewal settings are set to auto-renew, then you will be billed again on each renewal date based on the renewal settings.
+> A new commitment is billed up-front for the entire term. If the renewal settings are set to auto-renew, then you will be billed again on each renewal date based on the renewal settings.
 
 ### Edit an existing Provisioned Throughput commitment
 
-From the Manage Commitments view, you can also edit an existing commitment. There are two types of changes you can make to an existing commitment:
+From the **Manage Commitments** view, you can also edit an existing commitment. There are two types of changes you can make to an existing commitment:
 
 - You can add PTUs to the commitment.
 - You can change the renewal settings.
@@ -291,14 +291,14 @@ Adding PTUs to an existing commitment will allow you to create larger or more nu
 :::image type="content" source="../media/how-to/provisioned-onboarding/increase-commitment.png" alt-text="Screenshot of commitment purchase UI with an increase in the amount to commit value." lightbox="../media/how-to/provisioned-onboarding/increase-commitment.png":::
 
 > [!IMPORTANT]
-> When you add PTUs to a commitment, they will be billed immediately, at a pro-rated amount from the current date to the end of the existing commitment term.  Adding PTUs does not reset the commitment term.
+> When you add PTUs to a commitment, they will be billed immediately, at a pro-rated amount from the current date to the end of the existing commitment term. Adding PTUs doesn't reset the commitment term.
 
 ### Changing renewal settings
 
-Commitment renewal settings can be changed at any time before the expiration date of your commitment.  Reasons you might want to change the renewal settings include ending your use of provisioned throughput by setting the commitment to not autorenew, or to decrease usage of provisioned throughput by lowering the number of PTUs that will be committed in the next period.
+Commitment renewal settings can be changed at any time before the expiration date of your commitment. Reasons you might want to change the renewal settings include ending your use of provisioned throughput by setting the commitment to not autorenew, or to decrease usage of provisioned throughput by lowering the number of PTUs that will be committed in the next period.
 
 > [!IMPORTANT]
-> If you allow a commitment to expire or decrease in size such that the deployments under the resource require more PTUs than you have in your resource commitment, you will receive hourly overage charges for any excess PTUs.  For example, a resource that has deployments that total 500 PTUs and a commitment for 300 PTUs will generate hourly overage charges for 200 PTUs.
+> If you allow a commitment to expire or decrease in size such that the deployments under the resource require more PTUs than you have in your resource commitment, you will receive hourly overage charges for any excess PTUs. For example, a resource that has deployments that total 500 PTUs and a commitment for 300 PTUs will generate hourly overage charges for 200 PTUs.
 
 ## Monitor commitments and prevent unexpected billings
 
@@ -319,7 +319,7 @@ To end use of provisioned throughput, and prevent hourly overage charges after c
 
 **Move a commitment/deployment to a new resource in the same subscription/region**
 
-It isn't possible in Azure OpenAI Studio to directly *move* a deployment or a commitment to a new resource. Instead, a new deployment needs to be created on the target resource and traffic moved to it. There will need to be a commitment purchased established on the new resource to accomplish this. Because commitments are charged up-front for a 30-day period, it's necessary to time this move with the expiration of the original commitment to minimize overlap with the new commitment and “double-billing” during the overlap.
+It isn't possible in Azure AI Foundry to directly *move* a deployment or a commitment to a new resource. Instead, a new deployment needs to be created on the target resource and traffic moved to it. There will need to be a commitment purchased established on the new resource to accomplish this. Because commitments are charged up-front for a 30-day period, it's necessary to time this move with the expiration of the original commitment to minimize overlap with the new commitment and “double-billing” during the overlap.
 
 There are two approaches that can be taken to implement this transition.
 
@@ -357,4 +357,4 @@ The same approaches apply in moving the commitment and deployment within the reg
 
 ### View and edit an existing resource
 
-In Azure OpenAI Studio, select **Quota** > **Provisioned** > **Manage commitments** and select a resource with an existing commitment to view/change it. 
+In Azure AI Foundry, select **Management center** > **Quota** > **Provisioned** > **Manage commitments** and select a resource with an existing commitment to view/change it. 

@@ -67,5 +67,5 @@ The migration with downtime approach involves migrating existing provisioned dep
 - Once your new deployment has succeeded, you may resume inference traffic on the new global or data zone deployment.
 
 ## How do I migrate my existing Azure Reservation to the new Azure Reservation products?
-Azure Reservations for Azure OpenAI Service provisioned offers are specific to the provisioned deployment type. If the Azure Reservation purchased does not match the provisioned deployment type, the deployment will default to the hourly payment model. If you choose to migrate to global or data zone provisioned deployments, you may need to purchase a new Azure Reservation for these deployments to support additional discounts. For more information on how to purchase a new Azure Reservation or make changes to an existing Azure Reservation, see the [Azure Reservations for Azure OpenAI Service Provisioned guidance](https://aka.ms/aoai/reservation-transition).
+Azure Reservations for Azure OpenAI Service provisioned offers are specific to the provisioned deployment type. If the Azure Reservation purchased does not match the provisioned deployment type, the deployment will default to the hourly payment model. If you choose to migrate to global or data zone provisioned deployments, you might need to purchase a new Azure Reservation for these deployments to support additional discounts. For more information on how to purchase a new Azure Reservation or make changes to an existing Azure Reservation, see the [Azure Reservations for Azure OpenAI Service Provisioned guidance](https://aka.ms/aoai/reservation-transition).
 

@@ -65,7 +65,7 @@ It can be helpful to provide red teamers with:
 
 ### Plan: What to test
 
-Because an application is developed using a base model, you may need to test at several different layers:
+Because an application is developed using a base model, you might need to test at several different layers:
 
 - The LLM base model with its safety system in place to identify any gaps that may need to be addressed in the context of your application system. (Testing is usually done through an API endpoint.)
 

@@ -19,14 +19,14 @@ Use this article to learn about Azure OpenAI On Your Data, which makes it easier
 
 ## What is Azure OpenAI On Your Data
 
-Azure OpenAI On Your Data enables you to run advanced AI models such as GPT-35-Turbo and GPT-4 on your own enterprise data without needing to train or fine-tune models. You can chat on top of and analyze your data with greater accuracy. You can specify sources to support the responses based on the latest information available in your designated data sources. You can access Azure OpenAI On Your Data using a REST API, via the SDK or the web-based interface in the [Azure OpenAI Studio](https://oai.azure.com/). You can also create a web app that connects to your data to enable an enhanced chat solution or deploy it directly as a copilot in the Copilot Studio (preview).
+Azure OpenAI On Your Data enables you to run advanced AI models such as GPT-35-Turbo and GPT-4 on your own enterprise data without needing to train or fine-tune models. You can chat on top of and analyze your data with greater accuracy. You can specify sources to support the responses based on the latest information available in your designated data sources. You can access Azure OpenAI On Your Data using a REST API, via the SDK or the web-based interface in the [Azure AI Foundry portal](https://oai.azure.com/). You can also create a web app that connects to your data to enable an enhanced chat solution or deploy it directly as a copilot in the Copilot Studio (preview).
 
 ## Developing with Azure OpenAI On Your Data
 
 :::image type="content" source="../media/use-your-data/workflow-diagram.png" alt-text="A diagram showing an example workflow.":::
 
 Typically, the development process you'd use with Azure OpenAI On Your Data is:
-1. **Ingest**: Upload files using either Azure OpenAI Studio or the ingestion API. This enables your data to be cracked, chunked and embedded into an Azure AI Search instance that can be used by Azure OpenAI models. If you have an existing [supported data source](#supported-data-sources), you can also connect it directly.
+1. **Ingest**: Upload files using either Azure AI Foundry portal or the ingestion API. This enables your data to be cracked, chunked and embedded into an Azure AI Search instance that can be used by Azure OpenAI models. If you have an existing [supported data source](#supported-data-sources), you can also connect it directly.
 
 1. **Develop**: After trying Azure OpenAI On Your Data, begin developing your application using the available REST API and SDKs, which are available in several languages. It will create prompts and search intents to pass to the Azure OpenAI service.
 
@@ -39,7 +39,7 @@ Typically, the development process you'd use with Azure OpenAI On Your Data is:
     
     1. **Response generation**: The resulting data is submitted along with other information like the system message to the Large Language Model (LLM) and the response is sent back to the application.
 
-To get started, [connect your data source](../use-your-data-quickstart.md) using Azure OpenAI Studio and start asking questions and chatting on your data.
+To get started, [connect your data source](../use-your-data-quickstart.md) using Azure AI Foundry portal and start asking questions and chatting on your data.
 
 ## Azure Role-based access controls (Azure RBAC) for adding data sources
 
@@ -136,9 +136,9 @@ Azure OpenAI On Your Data lets you restrict the documents that can be used in re
 
 ### Index field mapping 
 
-If you're using your own index, you'll be prompted in the Azure OpenAI Studio to define which fields you want to map for answering questions when you add your data source. You can provide multiple fields for *Content data*, and should include all fields that have text pertaining to your use case. 
+If you're using your own index, you'll be prompted in the Azure AI Foundry portal to define which fields you want to map for answering questions when you add your data source. You can provide multiple fields for *Content data*, and should include all fields that have text pertaining to your use case. 
 
-:::image type="content" source="../media/use-your-data/index-data-mapping.png" alt-text="A screenshot showing the index field mapping options in Azure OpenAI Studio." lightbox="../media/use-your-data/index-data-mapping.png":::
+:::image type="content" source="../media/use-your-data/index-data-mapping.png" alt-text="A screenshot showing the index field mapping options in Azure AI Foundry portal." lightbox="../media/use-your-data/index-data-mapping.png":::
 
 In this example, the fields mapped to **Content data** and **Title** provide information to the model to answer questions. **Title** is also used to title citation text. The field mapped to **File name** generates the citation names in the response. 
 
@@ -189,10 +189,10 @@ You might want to use Azure Blob Storage as a data source if you want to connect
 
 To keep your Azure AI Search index up-to-date with your latest data, you can schedule an automatic index refresh rather than manually updating it every time your data is updated. Automatic index refresh is only available when you choose **Azure Blob Storage** as the data source. To enable an automatic index refresh:
 
-1. [Add a data source](../quickstart.md) using Azure OpenAI studio.
+1. [Add a data source](../quickstart.md) using Azure AI Foundry portal.
 1. Under **Select or add data source** select **Indexer schedule** and choose the refresh cadence you would like to apply.
 
-    :::image type="content" source="../media/use-your-data/indexer-schedule.png" alt-text="A screenshot of the indexer schedule in Azure OpenAI Studio." lightbox="../media/use-your-data/indexer-schedule.png":::
+    :::image type="content" source="../media/use-your-data/indexer-schedule.png" alt-text="A screenshot of the indexer schedule in Azure AI Foundry portal." lightbox="../media/use-your-data/indexer-schedule.png":::
 
 After the data ingestion is set to a cadence other than once, Azure AI Search indexers will be created with a schedule equivalent to `0.5 * the cadence specified`. This means that at the specified cadence, the indexers will pull, reprocess, and index the documents that were added or modified from the storage container. This process ensures that the updated data gets preprocessed and indexed in the final index at the desired cadence automatically. To update your data, you only need to upload the additional documents from the Azure portal. From the portal, select **Storage Account** > **Containers**. Select the name of the original container, then **Upload**. The index will pick up the files automatically after the scheduled refresh period. The intermediate assets created in the Azure AI Search resource won't be cleaned up after ingestion to allow for future runs. These assets are:
    - `{Index Name}-index`
@@ -221,9 +221,9 @@ To modify the schedule, you can use the [Azure portal](https://portal.azure.com/
 
 # [Upload files (preview)](#tab/file-upload)
 
-Using Azure OpenAI Studio, you can upload files from your machine to try Azure OpenAI On Your Data. You also have the option to create a new Azure Blob Storage account and Azure AI Search resource. The service then stores the files to an Azure storage container and performs ingestion from the container. You can use the [quickstart](../use-your-data-quickstart.md) article to learn how to use this data source option.
+Using Azure AI Foundry portal, you can upload files from your machine to try Azure OpenAI On Your Data. You also have the option to create a new Azure Blob Storage account and Azure AI Search resource. The service then stores the files to an Azure storage container and performs ingestion from the container. You can use the [quickstart](../use-your-data-quickstart.md) article to learn how to use this data source option.
 
-:::image type="content" source="../media/quickstarts/add-your-data-source.png" alt-text="A screenshot showing options for selecting a data source in Azure OpenAI Studio." lightbox="../media/quickstarts/add-your-data-source.png":::
+:::image type="content" source="../media/quickstarts/add-your-data-source.png" alt-text="A screenshot showing options for selecting a data source in Azure AI Foundry portal." lightbox="../media/quickstarts/add-your-data-source.png":::
 
 [!INCLUDE [ai-search-ingestion](../includes/ai-search-ingestion.md)]
 
@@ -305,7 +305,7 @@ Mapping these fields correctly helps ensure the model has better response and ci
 
 ### Use Elasticsearch as a data source via API  
 
-Along with using Elasticsearch databases in Azure OpenAI Studio, you can also use your Elasticsearch database using the [API](../references/elasticsearch.md). 
+Along with using Elasticsearch databases in Azure AI Foundry portal, you can also use your Elasticsearch database using the [API](../references/elasticsearch.md). 
 
 # [MongoDB Atlas (preview)](#tab/mongo-db-atlas)
 
@@ -360,15 +360,15 @@ When you add your MongoDB Atlas data source, you can specify data fields to prop
 
 ## Deploy to a copilot (preview), Teams app (preview), or web app 
 
-After you connect Azure OpenAI to your data, you can deploy it using the **Deploy to** button in Azure OpenAI Studio.
+After you connect Azure OpenAI to your data, you can deploy it using the **Deploy to** button in Azure AI Foundry portal.
 
-:::image type="content" source="../media/use-your-data/deploy-model.png" alt-text="A screenshot showing the model deployment button in Azure OpenAI Studio." lightbox="../media/use-your-data/deploy-model.png":::
+:::image type="content" source="../media/use-your-data/deploy-model.png" alt-text="A screenshot showing the model deployment button in Azure AI Foundry portal." lightbox="../media/use-your-data/deploy-model.png":::
 
 This gives you multiple options for deploying your solution.
 
 #### [Copilot (preview)](#tab/copilot)
 
-You can deploy to a copilot in [Copilot Studio](/microsoft-copilot-studio/fundamentals-what-is-copilot-studio) (preview) directly from Azure OpenAI Studio, enabling you to bring conversational experiences to various channels such as: Microsoft Teams, websites, Dynamics 365, and other [Azure Bot Service channels](/microsoft-copilot-studio/publication-connect-bot-to-azure-bot-service-channels). The tenant used in the Azure OpenAI service and Copilot Studio (preview) should be the same. For more information, see [Use a connection to Azure OpenAI On Your Data](/microsoft-copilot-studio/nlu-generative-answers-azure-openai).
+You can deploy to a copilot in [Copilot Studio](/microsoft-copilot-studio/fundamentals-what-is-copilot-studio) (preview) directly from Azure AI Foundry portal, enabling you to bring conversational experiences to various channels such as: Microsoft Teams, websites, Dynamics 365, and other [Azure Bot Service channels](/microsoft-copilot-studio/publication-connect-bot-to-azure-bot-service-channels). The tenant used in the Azure OpenAI service and Copilot Studio (preview) should be the same. For more information, see [Use a connection to Azure OpenAI On Your Data](/microsoft-copilot-studio/nlu-generative-answers-azure-openai).
 
 > [!NOTE]
 > Deploying to a copilot in Copilot Studio (preview) is only available in US regions.
@@ -390,15 +390,15 @@ A Teams app lets you bring conversational experience to your users in Teams to i
 - Your Azure account has been assigned **Cognitive Services OpenAI user** or **Cognitive Services OpenAI Contributor** role of the Azure OpenAI resource you're using, allowing your account to make Azure OpenAI API calls. For more information, see [Azure OpenAI On Your data configuration](../how-to/on-your-data-configuration.md#using-the-api) and [Add role assignment to an Azure OpenAI resource](/azure/ai-services/openai/how-to/role-based-access-control#add-role-assignment-to-an-azure-openai-resource) for instructions on setting this role in the Azure portal. 
 
 
-You can deploy to a standalone Teams app directly from Azure OpenAI Studio. Follow the steps below: 
+You can deploy to a standalone Teams app directly from Azure AI Foundry portal. Follow the steps below: 
 
 1. After you've added your data to the chat model, select **Deploy** and then **a new Teams app (preview)**. 
 
 1. Enter the name of your Teams app and download the resulting .zip file.
 
 1. Extract the .zip file and open the folder in Visual Studio Code.
 
-1. If you chose **API key** in the data connection step, manually copy and paste your Azure AI Search key into the `src\prompts\chat\config.json` file. Your Azure AI Search Key can be found in Azure OpenAI Studio Playground by selecting the **View code** button with the key located under Azure Search Resource Key. If you chose **System assigned managed identity**, you can skip this step. Learn more about different data connection options in the [Data connection](/azure/ai-services/openai/concepts/use-your-data?tabs=ai-search#data-connection) section.
+1. If you chose **API key** in the data connection step, manually copy and paste your Azure AI Search key into the `src\prompts\chat\config.json` file. Your Azure AI Search Key can be found in Azure AI Foundry portal Playground by selecting the **View code** button with the key located under Azure Search Resource Key. If you chose **System assigned managed identity**, you can skip this step. Learn more about different data connection options in the [Data connection](/azure/ai-services/openai/concepts/use-your-data?tabs=ai-search#data-connection) section.
 
 1. Open the Visual Studio Code terminal and log into Azure CLI, selecting the account that you assigned **Cognitive Service OpenAI User** role to. Use the `az login` command in the terminal to log in.
 
@@ -464,7 +464,7 @@ A small chunk size like 256 produces more granular chunks. This size also means
 
 ### Runtime parameters
 
-You can modify the following additional settings in the **Data parameters** section in Azure OpenAI Studio and [the API](../references/on-your-data.md). You don't need to reingest your data when you update these parameters. 
+You can modify the following additional settings in the **Data parameters** section in Azure AI Foundry portal and [the API](../references/on-your-data.md). You don't need to reingest your data when you update these parameters. 
 
 
 |Parameter name  | Description  |
@@ -481,9 +481,9 @@ It's possible for the model to return `"TYPE":"UNCITED_REFERENCE"` instead of `"
 
 You can define a system message to steer the model's reply when using Azure OpenAI On Your Data. This message allows you to customize your replies on top of the retrieval augmented generation (RAG) pattern that Azure OpenAI On Your Data uses. The system message is used in addition to an internal base prompt to provide the experience. To support this, we truncate the system message after a specific [number of tokens](#token-usage-estimation-for-azure-openai-on-your-data) to ensure the model can answer questions using your data. If you are defining extra behavior on top of the default experience, ensure that your system prompt is detailed and explains the exact expected customization. 
 
-Once you select add your dataset, you can use the **System message** section in the Azure OpenAI Studio, or the `role_information` [parameter in the API](../references/on-your-data.md).
+Once you select add your dataset, you can use the **System message** section in the Azure AI Foundry portal, or the `role_information` [parameter in the API](../references/on-your-data.md).
 
-:::image type="content" source="../media/use-your-data/system-message.png" alt-text="A screenshot showing the system message option in Azure OpenAI Studio." lightbox="../media/use-your-data/system-message.png":::
+:::image type="content" source="../media/use-your-data/system-message.png" alt-text="A screenshot showing the system message option in Azure AI Foundry portal." lightbox="../media/use-your-data/system-message.png":::
 
 #### Potential usage patterns
 
@@ -681,7 +681,7 @@ token_output = TokenEstimator.estimate_tokens(input_text)
 
 ## Troubleshooting 
 
-To troubleshoot failed operations, always look out for errors or warnings specified either in the API response or Azure OpenAI Studio. Here are some of the common errors and warnings: 
+To troubleshoot failed operations, always look out for errors or warnings specified either in the API response or Azure AI Foundry portal. Here are some of the common errors and warnings: 
 
 ### Failed ingestion jobs
 

@@ -1,7 +1,7 @@
 ---
 title: 'Quickstart: Generate images with Azure OpenAI Service'
 titleSuffix: Azure OpenAI
-description: Learn how to get started generating images with Azure OpenAI Service by using the Python SDK, the REST APIs, or Azure OpenAI Studio.
+description: Learn how to get started generating images with Azure OpenAI Service by using the Python SDK, the REST APIs, or Azure AI Foundry portal.
 #services: cognitive-services
 manager: nitinme
 ms.service: azure-ai-openai
@@ -16,11 +16,11 @@ zone_pivot_groups: openai-quickstart-dall-e
 # Quickstart: Generate images with Azure OpenAI Service
 
 > [!NOTE]
-> The image generation API creates an image from a text prompt. It does not edit or create variations from existing images.
+> The image generation API creates an image from a text prompt. It doesn't edit or create variations from existing images.
 
 ::: zone pivot="programming-language-studio"
 
-[!INCLUDE [Studio quickstart](includes/dall-e-studio.md)]
+[!INCLUDE [Portal quickstart](includes/dall-e-studio.md)]
 
 ::: zone-end
 

@@ -1,7 +1,7 @@
 ---
 title: 'Customize a model with Azure OpenAI Service'
 titleSuffix: Azure OpenAI
-description: Learn how to create your own customized model with Azure OpenAI Service by using Python, the REST APIs, or Azure OpenAI Studio.
+description: Learn how to create your own customized model with Azure OpenAI Service by using Python, the REST APIs, or Azure AI Foundry portal.
 #services: cognitive-services
 manager: nitinme
 ms.service: azure-ai-openai
@@ -28,7 +28,7 @@ We use LoRA, or low rank approximation, to fine-tune models in a way that reduce
 
 ::: zone pivot="programming-language-studio"
 
-[!INCLUDE [Azure OpenAI Studio fine-tuning](../includes/fine-tuning-unified.md)]
+[!INCLUDE [Azure AI Foundry portal fine-tuning](../includes/fine-tuning-unified.md)]
 
 ::: zone-end
 
@@ -159,7 +159,7 @@ In order to successfully access fine-tuning, you need **Cognitive Services OpenA
 
 ### Why did my upload fail?
 
-If your file upload fails in Azure OpenAI Studio, you can view the error message under “data files” in Azure OpenAI Studio. Hover your mouse over where it says “error” (under the status column) and an explanation of the failure will be displayed.
+If your file upload fails in Azure AI Foundry portal, you can view the error message under **Data files** in Azure AI Foundry portal. Hover your mouse over where it says “error” (under the status column) and an explanation of the failure will be displayed.
 
 :::image type="content" source="../media/fine-tuning/error.png" alt-text="Screenshot of fine-tuning error message." lightbox="../media/fine-tuning/error.png":::
 

@@ -69,7 +69,7 @@ After you configure the diagnostic settings, you can work with metrics and log d
 
 [!INCLUDE [horz-monitor-kusto-queries](~/reusable-content/ce-skilling/azure/includes/azure-monitor/horizontals/horz-monitor-kusto-queries.md)]
 
-After you deploy an Azure OpenAI model, you can send some completions calls by using the **playground** environment in [Azure AI Foundry](https://oai.azure.com/).
+After you deploy an Azure OpenAI model, you can send some completions calls by using the **playground** environment in [Azure AI Foundry](https://ai.azure.com/).
 
 Any text that you enter in the **Completions playground** or the **Chat completions playground** generates metrics and log data for your Azure OpenAI resource. In the Log Analytics workspace for your resource, you can query the monitoring data by using the [Kusto](/azure/data-explorer/kusto/query/) query language.
 

@@ -67,9 +67,9 @@ Azure OpenAI On Your Data lets you restrict the documents that can be used in re
     `group_ids` is the default field name. If you use a different field name like `my_group_ids`, you can map the field in [index field mapping](../concepts/use-your-data.md#index-field-mapping).
 
 1. Make sure each sensitive document in the index has this security field value set to the permitted groups of the document.
-1. In [Azure OpenAI Studio](https://oai.azure.com/portal), add your data source. in the [index field mapping](../concepts/use-your-data.md#index-field-mapping) section, you can map zero or one value to the **permitted groups** field, as long as the schema is compatible. If the **permitted groups** field isn't mapped, document level access is disabled. 
+1. In [Azure AI Foundry portal](https://oai.azure.com/portal), add your data source. in the [index field mapping](../concepts/use-your-data.md#index-field-mapping) section, you can map zero or one value to the **permitted groups** field, as long as the schema is compatible. If the **permitted groups** field isn't mapped, document level access is disabled. 
 
-**Azure OpenAI Studio**
+**Azure AI Foundry portal**
 
 Once the Azure AI Search index is connected, your responses in the studio have document access based on the Microsoft Entra permissions of the logged in user.
 
@@ -178,7 +178,7 @@ This step can be skipped only if you have a [shared private link](#create-shared
 
 You can disable public network access of your Azure OpenAI resource in the Azure portal. 
 
-To allow access to your Azure OpenAI Service from your client machines, like using Azure OpenAI Studio, you need to create [private endpoint connections](/azure/ai-services/cognitive-services-virtual-networks?tabs=portal#use-private-endpoints) that connect to your Azure OpenAI resource.
+To allow access to your Azure OpenAI Service from your client machines, like using Azure AI Foundry portal, you need to create [private endpoint connections](/azure/ai-services/cognitive-services-virtual-networks?tabs=portal#use-private-endpoints) that connect to your Azure OpenAI resource.
 
 
 ## Configure Azure AI Search
@@ -202,7 +202,7 @@ For more information, see the [Azure AI Search RBAC article](/azure/search/searc
 
 You can disable public network access of your Azure AI Search resource in the Azure portal. 
 
-To allow access to your Azure AI Search resource from your client machines, like using Azure OpenAI Studio, you need to create [private endpoint connections](/azure/search/service-create-private-endpoint) that connect to your Azure AI Search resource.
+To allow access to your Azure AI Search resource from your client machines, like using Azure AI Foundry portal, you need to create [private endpoint connections](/azure/search/service-create-private-endpoint) that connect to your Azure AI Search resource.
 
 
 ### Enable trusted service
@@ -256,7 +256,7 @@ In the Azure portal, navigate to your storage account networking tab, choose "Se
 
 You can disable public network access of your Storage Account in the Azure portal. 
 
-To allow access to your Storage Account from your client machines, like using Azure OpenAI Studio, you need to create [private endpoint connections](/azure/storage/common/storage-private-endpoints) that connect to your blob storage.
+To allow access to your Storage Account from your client machines, like using Azure AI Foundry portal, you need to create [private endpoint connections](/azure/storage/common/storage-private-endpoints) that connect to your blob storage.
 
 
 
@@ -285,9 +285,9 @@ To enable the developers to use these resources to build applications, the admin
 
 |Role| Resource | Description |
 |--|--|--|
-| `Cognitive Services OpenAI Contributor` | Azure OpenAI | Call public ingestion API from Azure OpenAI Studio. The `Contributor` role is not enough, because if you only have `Contributor` role, you cannot call data plane API via Microsoft Entra ID authentication, and Microsoft Entra ID authentication is required in the secure setup described in this article. |
-| `Contributor` | Azure AI Search | List API-Keys to list indexes from Azure OpenAI Studio.|
-| `Contributor` | Storage Account | List Account SAS to upload files from Azure OpenAI Studio.|
+| `Cognitive Services OpenAI Contributor` | Azure OpenAI | Call public ingestion API from Azure AI Foundry portal. The `Contributor` role is not enough, because if you only have `Contributor` role, you cannot call data plane API via Microsoft Entra ID authentication, and Microsoft Entra ID authentication is required in the secure setup described in this article. |
+| `Contributor` | Azure AI Search | List API-Keys to list indexes from Azure AI Foundry portal.|
+| `Contributor` | Storage Account | List Account SAS to upload files from Azure AI Foundry portal.|
 | `Contributor` | The resource group or Azure subscription where the developer need to deploy the web app to | Deploy web app to the developer's Azure subscription.|
 | `Role Based Access Control Administrator` | Azure OpenAI | Permission to configure the necessary role assignment on the Azure OpenAI resource. Enables the web app to call Azure OpenAI. |
 
@@ -309,9 +309,9 @@ Configure your local machine `hosts` file to point your resources host names to
 10.0.0.7 contoso.blob.core.windows.net
 ```
 
-## Azure OpenAI Studio
+## Azure AI Foundry portal
 
-You should be able to use all Azure OpenAI Studio features, including both ingestion and inference, from your on-premises client machines.
+You should be able to use all Azure AI Foundry portal features, including both ingestion and inference, from your on-premises client machines.
 
 ## Web app
 The web app communicates with your Azure OpenAI resource. Since your Azure OpenAI resource has public network disabled, the web app needs to be set up to use the private endpoint in your virtual network to access your Azure OpenAI resource.
@@ -322,7 +322,7 @@ The web app needs to resolve your Azure OpenAI host name to the private IP of th
 1. [Add a DNS record](/azure/dns/private-dns-getstarted-portal#create-an-additional-dns-record). The IP is the private IP of the private endpoint for your Azure OpenAI resource, and you can get the IP address from the network interface associated with the private endpoint for your Azure OpenAI.
 1. [Link the private DNS zone to your virtual network](/azure/dns/private-dns-getstarted-portal#link-the-virtual-network) so the web app integrated in this virtual network can use this private DNS zone.
 
-When deploying the web app from Azure OpenAI Studio, select the same location with the virtual network, and select a proper SKU, so it can support the [virtual network integration feature](/azure/app-service/overview-vnet-integration). 
+When deploying the web app from Azure AI Foundry portal, select the same location with the virtual network, and select a proper SKU, so it can support the [virtual network integration feature](/azure/app-service/overview-vnet-integration). 
 
 After the web app is deployed, from the Azure portal networking tab, configure the web app outbound traffic virtual network integration, choose the third subnet that you reserved for web app.
 

@@ -22,7 +22,6 @@ The following guide walks you through key steps in creating a provisioned deploy
 
 - An Azure subscription - [Create one for free](https://azure.microsoft.com/free/cognitive-services?azure-portal=true)
 - Azure Contributor or Cognitive Services Contributor role 
-- Access to Azure OpenAI Studio
 
 ## Obtain/verify PTU quota availability.
 
@@ -37,9 +36,9 @@ Creating a new deployment requires available (unused) quota to cover the desired
 
 Then 200 PTUs of quota are considered used, and there are 300 PTUs available for use to create new deployments. 
 
-A default amount of global, data zone, and regional provisioned quota is assigned to eligible subscriptions in several regions. You can view the quota available to you in a region by visiting the Quotas pane in Azure AI Foundry and selecting the desired subscription and region. For example, the screenshot below shows a quota limit of 500 PTUs in West US for the selected subscription. Note that you might see lower values of available default quotas. 
+A default amount of global, data zone, and regional provisioned quota is assigned to eligible subscriptions in several regions. You can view the quota available to you in a region by visiting the Quotas pane in Azure AI Foundry portal and selecting the desired subscription and region. For example, the screenshot below shows a quota limit of 500 PTUs in West US for the selected subscription. Note that you might see lower values of available default quotas. 
 
-:::image type="content" source="../media/provisioned/available-quota.png" alt-text="A screenshot of the available quota in Azure OpenAI studio." lightbox="../media/provisioned/available-quota.png":::
+:::image type="content" source="../media/provisioned/available-quota.png" alt-text="A screenshot of the available quota in Azure AI Foundry portal." lightbox="../media/provisioned/available-quota.png":::
 
 Additional quota can be requested by clicking the Request Quota link to the right of the “Usage/Limit” column.  (This is off-screen in the screenshot above). 
 
@@ -54,16 +53,15 @@ Provisioned deployments are created via Azure OpenAI resource objects within Azu
 
 once you have verified your quota, you can create a deployment. To create a provisioned deployment, you can follow these steps; the choices described reflect the entries shown in the screenshot. 
 
-:::image type="content" source="../media/provisioned/deployment-screen.png" alt-text="Screenshot of the Azure OpenAI Studio deployment page for a provisioned deployment." lightbox="../media/provisioned/deployment-screen.png":::
+:::image type="content" source="../media/provisioned/deployment-screen.png" alt-text="Screenshot of the Azure AI Foundry portal deployment page for a provisioned deployment." lightbox="../media/provisioned/deployment-screen.png":::
 
 
 
-1.	Sign into [Azure AI Foundry](https://oai.azure.com)
+1. Sign into the [Azure AI Foundry portal](https://ai.azure.com).
 1. Choose the subscription that was enabled for provisioned deployments & select the desired resource in a region where you have the quota.
-
-3.	Under **Management** in the left-nav select **Deployments**.
-4.	Select Create new deployment and configure the following fields. Expand the **advanced options** drop-down menu.
-5.	Fill out the values in each field. Here's an example:
+1. Under **Management** in the left-nav select **Deployments**.
+1. Select Create new deployment and configure the following fields. Expand the **advanced options** drop-down menu.
+1. Fill out the values in each field. Here's an example:
 
 | Field | Description |	Example |
 |--|--|--|
@@ -74,8 +72,8 @@ once you have verified your quota, you can create a deployment. To create a prov
 | Deployment Type	|This impacts the throughput and performance. Choose Global Provisioned-Managed, DataZone Provisioned-Managed or Provisioned-Managed from the deployment dialog dropdown for your deployment 	| Provisioned-Managed |
 | Provisioned Throughput Units |	Choose the amount of throughput you wish to include in the deployment. |	100 |
 
-Important things to note: 
-* The deployment dialog contains a reminder that you can purchase an Azure Reservation for Azure OpenAI Provisioned to obtain a significant discount for a term commitment. 
+> [!NOTE]
+> The deployment dialog contains a reminder that you can purchase an Azure Reservation for Azure OpenAI Provisioned to obtain a significant discount for a term commitment. 
 
 Once you have entered the deployment settings, click **Confirm Pricing** to continue.  A pricing confirmation dialog will appear that will display the list price for the deployment, if you choose to pay for it on an hourly basis, with no Azure Reservation to provide a term discount.
 
@@ -108,16 +106,16 @@ REST, ARM template, Bicep, and Terraform can also be used to create deployments.
 
 Due to the dynamic nature of capacity availability, it is possible that the region of your selected resource might not have the service capacity to create the deployment of the specified model, version, and number of PTUs. 
 
-In this event, Azure AI Foundry will direct you to other regions with available quota and capacity to create a deployment of the desired model. If this happens, the deployment dialog will look like this: 
+In this event, the wizard in Azure AI Foundry portal will direct you to other regions with available quota and capacity to create a deployment of the desired model. If this happens, the deployment dialog will look like this: 
 
-:::image type="content" source="../media/provisioned/deployment-screen-2.png" alt-text="Screenshot of the Azure OpenAI Studio deployment page for a provisioned deployment with no capacity available." lightbox="../media/provisioned/deployment-screen-2.png":::
+:::image type="content" source="../media/provisioned/deployment-screen-2.png" alt-text="Screenshot of the Azure AI Foundry portal deployment page for a provisioned deployment with no capacity available." lightbox="../media/provisioned/deployment-screen-2.png":::
 
 Things to notice: 
 
 * A message displays showing you many PTUs you have in available quota, and how many can currently be deployed at this time. 
 * If you select a number of PTUs greater than service capacity, a message will appear that provides options for you to obtain more capacity, and a button to allow you to select an alternate region. Clicking the "See other regions" button will display a dialog that shows a list of Azure OpenAI resources where you can create a deployment, along with the maximum sized deployment that can be created based on available quota and service capacity in each region. 
 
-:::image type="content" source="../media/provisioned/choose-different-resource.png" alt-text="Screenshot of the Azure OpenAI Studio deployment page for choosing a different resource and region." lightbox="../media/provisioned/choose-different-resource.png":::
+:::image type="content" source="../media/provisioned/choose-different-resource.png" alt-text="Screenshot of the Azure AI Foundry portal deployment page for choosing a different resource and region." lightbox="../media/provisioned/choose-different-resource.png":::
 
 Selecting a resource and clicking **Switch resource** will cause the deployment dialog to redisplay using the selected resource. You can then proceed to create your deployment in the new region. 
 
@@ -167,9 +165,8 @@ The inferencing code for provisioned deployments is the same a standard deployme
 
 ## Understanding expected throughput
 The amount of throughput that you can achieve on the endpoint is a factor of the number of PTUs deployed, input size, output size, and call rate. The number of concurrent calls and total tokens processed can vary based on these values. Our recommended way for determining the throughput for your deployment is as follows:
-1. Use the Capacity calculator for a sizing estimate. You can find the capacity calculator in Azure AI Foundry under the quotas page and Provisioned tab.  
-
-2.	Benchmark the load using real traffic workload. For more information about benchmarking, see the [benchmarking](#run-a-benchmark) section.
+1. Use the Capacity calculator for a sizing estimate. You can find the capacity calculator in Azure AI Foundry portal under the quotas page and Provisioned tab.  
+1. Benchmark the load using real traffic workload. For more information about benchmarking, see the [benchmarking](#run-a-benchmark) section.
 
 
 ## Measuring your deployment utilization

@@ -12,15 +12,15 @@ manager: nitinme
 
 # Use Risks & Safety monitoring in Azure AI Foundry (preview) 
 
-When you use an Azure OpenAI model deployment with a content filter, you may want to check the results of the filtering activity. You can use that information to further adjust your [filter configuration](/azure/ai-services/openai/how-to/content-filters) to serve your specific business needs and Responsible AI principles.  
+When you use an Azure OpenAI model deployment with a content filter, you might want to check the results of the filtering activity. You can use that information to further adjust your [filter configuration](/azure/ai-services/openai/how-to/content-filters) to serve your specific business needs and Responsible AI principles.  
 
-[Azure AI Foundry](https://oai.azure.com/) provides a Risks & Safety monitoring dashboard for each of your deployments that uses a content filter configuration.
+[Azure AI Foundry](https://ai.azure.com/) provides a Risks & Safety monitoring dashboard for each of your deployments that uses a content filter configuration.
 
 ## Access Risks & Safety monitoring
 
 To access Risks & Safety monitoring, you need an Azure OpenAI resource in one of the supported Azure regions: East US, Switzerland North, France Central, Sweden Central, Canada East. You also need a model deployment that uses a content filter configuration.
 
-Go to [Azure AI Foundry](https://oai.azure.com/) and sign in with the credentials associated with your Azure OpenAI resource. Select a project. Then select the **Models + endpoints** tab on the left and then select your model deployment from the list. On the deployment's page, select the **Metrics** tab at the top. Then select **Open in Azure Monitor** to view the full report in the Azure portal.
+Go to [Azure AI Foundry](https://ai.azure.com/) and sign in with the credentials associated with your Azure OpenAI resource. Select a project. Then select the **Models + endpoints** tab on the left and then select your model deployment from the list. On the deployment's page, select the **Metrics** tab at the top. Then select **Open in Azure Monitor** to view the full report in the Azure portal.
 
 ## Configure metrics   
 

@@ -33,7 +33,7 @@ Support for structured outputs was first added in API version `2024-08-01-previe
 
 # [Python (Microsoft Entra ID)](#tab/python-secure)
 
-You can use [`Pydantic`](https://docs.pydantic.dev/latest/) to define object schemas in Python. Depending on what version of the [OpenAI](https://pypi.org/project/openai/) and [`Pydantic` libraries](https://pypi.org/project/pydantic/) you're running you may need to upgrade to a newer version. These examples were tested against `openai 1.42.0` and `pydantic 2.8.2`.
+You can use [`Pydantic`](https://docs.pydantic.dev/latest/) to define object schemas in Python. Depending on what version of the [OpenAI](https://pypi.org/project/openai/) and [`Pydantic` libraries](https://pypi.org/project/pydantic/) you're running you might need to upgrade to a newer version. These examples were tested against `openai 1.42.0` and `pydantic 2.8.2`.
 
 ```cmd
 pip install openai pydantic --upgrade
@@ -120,7 +120,7 @@ name='Science Fair' date='Friday' participants=['Alice', 'Bob']
 
 # [Python (key-based auth)](#tab/python)
 
-You can use [`Pydantic`](https://docs.pydantic.dev/latest/) to define object schemas in Python. Depending on what version of the [OpenAI](https://pypi.org/project/openai/) and [`Pydantic` libraries](https://pypi.org/project/pydantic/) you're running you may need to upgrade to a newer version. These examples were tested against `openai 1.42.0` and `pydantic 2.8.2`.
+You can use [`Pydantic`](https://docs.pydantic.dev/latest/) to define object schemas in Python. Depending on what version of the [OpenAI](https://pypi.org/project/openai/) and [`Pydantic` libraries](https://pypi.org/project/pydantic/) you're running you might need to upgrade to a newer version. These examples were tested against `openai 1.42.0` and `pydantic 2.8.2`.
 
 ```cmd
 pip install openai pydantic --upgrade

@@ -13,7 +13,7 @@ ms.author: pafarley
 
 # Use a blocklist with Azure OpenAI
 
-The configurable content filters are sufficient for most content moderation needs. However, you may need to filter terms specific to your use case. 
+The configurable content filters are sufficient for most content moderation needs. However, you might need to filter terms specific to your use case. 
 
 ## Prerequisites
 

@@ -7,23 +7,23 @@ ms.service: azure-ai-openai
 ms.topic: how-to
 author: aahill
 ms.author: aahi
-ms.date: 08/09/2024
+ms.date: 01/08/2025
 recommendations: false
 ---
 
 
 # Use the Azure OpenAI web app
 
-Along with Azure AI Foundry, Azure OpenAI Studio, APIs, and SDKs, you can use the customizable standalone web app to interact with Azure OpenAI models by using a graphical user interface. Key features include:
+Along with Azure AI Foundry portal, APIs, and SDKs, you can use the customizable standalone web app to interact with Azure OpenAI models by using a graphical user interface. Key features include:
 * Connectivity with multiple data sources to support rich querying and retrieval-augmented generation, including Azure AI Search, Prompt Flow, and more.
 * Conversation history and user feedback collection through Cosmos DB.
 * Authentication with role-based access control via Microsoft Entra ID.
 * Customization of the user interface, data sources, and features using environment variables (no-code via Azure portal).
-* Support for modifying the underlying web application source code as an open-source repository. 
+* Sample source code for the web app is available on [GitHub](https://github.com/microsoft/sample-app-aoai-chatGPT). Source code is provided "as is" and as a sample only. Customers are responsible for all customization and implementation of their web apps.
 
-You can deploy the app by using either [Azure AI Foundry](/azure/ai-studio/tutorials/deploy-chat-web-app) or [Azure OpenAI Studio](/azure/ai-services/openai/use-your-data-quickstart), or through a manual deployment through the Azure portal or the Azure Developer CLI via your local machine [(instructions available at the repository here)](https://github.com/microsoft/sample-app-aoai-chatGPT). Depending on your deployment channel, you can preload a data source to chat with via the web application, but this can be changed after deployment. 
+You can deploy the app via the [Azure AI Foundry portal](/azure/ai-studio/tutorials/deploy-chat-web-app), the [Azure portal](https://portal.azure.com), or the Azure Developer CLI via your local machine [(instructions available at the repository here)](https://github.com/microsoft/sample-app-aoai-chatGPT). Depending on your deployment channel, you can preload a data source to chat with via the web application, but this can be changed after deployment. 
 
-For Azure OpenAI beginners aspiring to chat with their data through the web application, [Azure AI Foundry](/azure/ai-studio/tutorials/deploy-chat-web-app) is the recommended medium for initial deployment and data source configuration.
+For Azure OpenAI beginners aspiring to chat with their data through the web application, [Azure AI Foundry portal](/azure/ai-studio/tutorials/deploy-chat-web-app) is the recommended medium for initial deployment and data source configuration.
 
 ![Screenshot that shows the web app interface.](../media/use-your-data/web-app.png)
 
@@ -59,8 +59,6 @@ When you're customizing the app, we recommend:
 
 - Updating the app settings for each of your deployed apps to use new API keys after you rotate keys for your Azure OpenAI or Azure AI Search resource.
 
-Sample source code for the web app is available on [GitHub](https://github.com/microsoft/sample-app-aoai-chatGPT). Source code is provided "as is" and as a sample only. Customers are responsible for all customization and implementation of their web apps.
-
 ## Modifying the application user interface
 
 The environment variables relevant to user interface customization are:
@@ -119,7 +117,7 @@ To modify the application user interface, follow the instructions in the previou
 
 You can turn on chat history for your users of the web app. When you turn on the feature, users have access to their individual previous queries and responses.
 
-To turn on chat history, deploy or redeploy your model as a web app by using [Azure OpenAI Studio](https://oai.azure.com/portal) or [Azure AI Foundry](https://ai.azure.com/) and select **Enable chat history and user feedback in the web app**.
+To turn on chat history, deploy or redeploy your model as a web app by using [Azure AI Foundry portal](https://ai.azure.com/) and select **Enable chat history and user feedback in the web app**.
 
 :::image type="content" source="../media/use-your-data/enable-chat-history.png" alt-text="Screenshot of the checkbox for enabling chat history in Azure OpenAI or Azure AI Foundry." lightbox="../media/use-your-data/enable-chat-history.png":::
 
@@ -164,13 +162,9 @@ This can be accomplished using the Advanced edit or simple Edit options as previ
 
 ## Connecting to Azure AI Search and uploaded files as a data source
 
-### Using Azure AI Foundry
-
-Follow [this tutorial on integrating Azure AI Search with Azure AI Foundry](/azure/ai-studio/tutorials/deploy-chat-web-app#add-your-data-and-try-the-chat-model-again) and redeploy your application.
-
-### Using Azure OpenAI Studio
+### Using Azure AI Foundry portal
 
-Follow [this tutorial on integrating Azure AI Search with OpenAI Studio](/azure/ai-services/openai/use-your-data-quickstart#add-your-data-using-azure-openai-studio) and redeploy your application.
+Follow [this tutorial on integrating Azure AI Search with Azure AI Foundry](/azure/ai-studio/tutorials/deploy-chat-web-app#add-your-data-and-try-the-chat-model-again) or [quickstart](/azure/ai-services/openai/use-your-data-quickstart#add-your-data) and redeploy your application.
 
 ### Using environment variables
 
@@ -181,7 +175,7 @@ To connect to Azure AI Search without redeploying your app, you can modify the f
     - Data type: text
 - `AZURE_SEARCH_INDEX`: This is the name of your Azure AI Search instance's index name.
     - Data type: text
-- `AZURE_SEARCH_KEY`: This is the authentication key of your Azure AI Search instance. Optional if using Microsoft Entra ID for authentication.
+- `AZURE_SEARCH_KEY`: This is the authentication key of your Azure AI Search instance. Optional if using the recommended Microsoft Entra ID for authentication.
     - Data type: text
 
 ### Further customization scenarios using environment variables
@@ -195,15 +189,15 @@ To connect to Azure AI Search without redeploying your app, you can modify the f
 - `AZURE_SEARCH_ENABLE_IN_DOMAIN`: Limits responses to queries related only to your data.
     - Data type: boolean, should be set to `True`.
 - `AZURE_SEARCH_CONTENT_COLUMNS`: Specifies the list of fields in your Azure AI Search index that contain the text content of your documents, used when formulating a bot response.
-    - Data type: text, defaults to `content` if deployed from Azure AI Foundry or Azure OpenAI Studio,
+    - Data type: text, defaults to `content` if deployed from Azure AI Foundry portal,
 - `AZURE_SEARCH_FILENAME_COLUMN`: Specifies the field from your Azure AI Search index that provides a unique identifier of the source data to display in the UI.
-    - Data type: text, defaults to `filepath` if deployed from Azure AI Foundry or Azure OpenAI Studio,
+    - Data type: text, defaults to `filepath` if deployed from Azure AI Foundry portal,
 - `AZURE_SEARCH_TITLE_COLUMN`: Specifies the field from your Azure AI Search index that provides a relevant title or header for your data content to display in the UI.
-    - Data type: text, defaults to `title` if deployed from Azure AI Foundry or Azure OpenAI Studio,
+    - Data type: text, defaults to `title` if deployed from Azure AI Foundry portal,
 - `AZURE_SEARCH_URL_COLUMN`: Specifies the field from your Azure AI Search index that contains a URL for the document.
-    - Data type: text, defaults to `url` if deployed from Azure AI Foundry or Azure OpenAI Studio,
+    - Data type: text, defaults to `url` if deployed from Azure AI Foundry portal,
 - `AZURE_SEARCH_VECTOR_COLUMNS`: Specifies the list of fields in your Azure AI Search index that contain vector embeddings of your documents, used when formulating a bot response.
-    - Data type: text, defaults to `contentVector` if deployed from Azure AI Foundry or Azure OpenAI Studio,
+    - Data type: text, defaults to `contentVector` if deployed from Azure AI Foundry portal,
 - `AZURE_SEARCH_QUERY_TYPE`: Specifies the query type to use: `simple`, `semantic`, `vector`, `vectorSimpleHybrid`, or `vectorSemanticHybrid`. This setting takes precedence over `AZURE_SEARCH_USE_SEMANTIC_SEARCH`.
     - Data type: text, we recommend testing with `vectorSemanticHybrid`.
 - `AZURE_SEARCH_PERMITTED_GROUPS_COLUMN`: Specifies the field from your Azure AI Search index that contains Microsoft Entra group IDs, determining document-level access control.
@@ -408,7 +402,7 @@ If you customized or changed the app's source code, you need to update your app'
 
 ## Deleting your Cosmos DB instance
 
-Deleting your web app doesn't delete your Cosmos DB instance automatically. To delete your Cosmos DB instance along with all stored chats, you need to go to the associated resource in the [Azure portal](https://portal.azure.com) and delete it. If you delete the Cosmos DB resource but keep the chat history option selected on subsequent updates from the Azure OpenAI Studio, the application notifies the user of a connection error. However, the user can continue to use the web app without access to the chat history.
+Deleting your web app doesn't delete your Cosmos DB instance automatically. To delete your Cosmos DB instance along with all stored chats, you need to go to the associated resource in the [Azure portal](https://portal.azure.com) and delete it. If you delete the Cosmos DB resource but keep the chat history option selected on subsequent updates from the Azure AI Foundry portal, the application notifies the user of a connection error. However, the user can continue to use the web app without access to the chat history.
 
 ## Enabling Microsoft Entra ID authentication between services
 

@@ -22,7 +22,7 @@ You can get a list of models that are available for both inference and fine-tuni
 
 Azure OpenAI now supports automatic updates for select model deployments. On models where automatic update support is available, a model version drop-down is visible in Azure AI Foundry portal under **Deployments** and **Edit**:
 
-:::image type="content" source="../media/models/auto-update-new.png" alt-text="Screenshot of the deploy model UI of Azure AI Foundry." lightbox="../media/models/auto-update-new.png":::
+:::image type="content" source="../media/models/auto-update-new.png" alt-text="Screenshot of the deploy model UI in the Azure AI Foundry portal." lightbox="../media/models/auto-update-new.png":::
 
 You can learn more about Azure OpenAI model versions and how they work in the [Azure OpenAI model versions](../concepts/model-versions.md) article.
 
@@ -40,13 +40,13 @@ When you select a specific model version for a deployment, this version remains
 
 ## Viewing retirement dates
 
-For currently deployed models, from Azure AI Foundry select **Deployments**:
+For currently deployed models, in the Azure AI Foundry portal select **Deployments**:
 
-:::image type="content" source="../media/models/deployments-new.png" alt-text="Screenshot of the deployment UI of Azure AI Foundry." lightbox="../media/models/deployments-new.png":::
+:::image type="content" source="../media/models/deployments-new.png" alt-text="Screenshot of the deployment UI of the Azure AI Foundry portal." lightbox="../media/models/deployments-new.png":::
 
 ## Model deployment upgrade configuration
 
-You can check what model upgrade options are set for previously deployed models in [Azure AI Foundry](https://oai.azure.com). Select **Deployments** > Under the deployment name column select one of the deployment names that are highlighted in blue.
+You can check what model upgrade options are set for previously deployed models in the [Azure AI Foundry portal](https://ai.azure.com). Select **Deployments** > Under the deployment name column select one of the deployment names that are highlighted in blue.
 
 Selecting a deployment name opens the **Properties** for the model deployment. You can view what upgrade options are set for your deployment under **Version update policy**:
 

@@ -5,7 +5,7 @@ description: Information on the division of control plane and data plane API sur
 manager: nitinme
 ms.service: azure-ai-openai
 ms.topic: include
-ms.date: 11/01/2024
+ms.date: 01/08/2024
 ---
 
 
@@ -22,8 +22,8 @@ Each API surface/specification encapsulates a different set of Azure OpenAI capa
 | API | Latest preview release | Latest GA release | Specifications | Description |
 |:---|:----|:----|:----|:---|
 | **Control plane** | [`2024-06-01-preview`](/rest/api/aiservices/accountmanagement/operation-groups?view=rest-aiservices-accountmanagement-2024-06-01-preview&preserve-view=true) | [`2024-10-01`](/rest/api/aiservices/accountmanagement/deployments/create-or-update?view=rest-aiservices-accountmanagement-2024-10-01&tabs=HTTP&preserve-view=true) | [Spec files](https://github.com/Azure/azure-rest-api-specs/tree/main/specification/cognitiveservices/resource-manager/Microsoft.CognitiveServices) | Azure OpenAI shares a common control plane with all other Azure AI Services. The control plane API is used for things like [creating Azure OpenAI resources](/rest/api/aiservices/accountmanagement/accounts/create?view=rest-aiservices-accountmanagement-2023-05-01&tabs=HTTP&preserve-view=true), [model deployment](/rest/api/aiservices/accountmanagement/deployments/create-or-update?view=rest-aiservices-accountmanagement-2023-05-01&tabs=HTTP&preserve-view=true), and other higher level resource management tasks. The control plane also governs what is possible to do with capabilities like Azure Resource Manager, Bicep, Terraform, and Azure CLI.|
-| **Data plane - authoring** | `2024-10-01-preview` | `2024-10-21` | [Spec files](https://github.com/Azure/azure-rest-api-specs/tree/main/specification/cognitiveservices/data-plane/AzureOpenAI/authoring) | The data plane authoring API controls [fine-tuning](/rest/api/azureopenai/fine-tuning?view=rest-azureopenai-2024-08-01-preview&preserve-view=true), [file-upload](/rest/api/azureopenai/files/upload?view=rest-azureopenai-2024-08-01-preview&tabs=HTTP&preserve-view=true), [ingestion jobs](/rest/api/azureopenai/ingestion-jobs/create?view=rest-azureopenai-2024-08-01-preview&tabs=HTTP&preserve-view=true), [batch](/rest/api/azureopenai/batch?view=rest-azureopenai-2024-08-01-preview&tabs=HTTP&preserve-view=true) and certain [model level queries](/rest/api/azureopenai/models/get?view=rest-azureopenai-2024-08-01-preview&tabs=HTTP&preserve-view=true)
-| **Data plane - inference** | [`2024-10-01-preview`](/azure/ai-services/openai/reference-preview#data-plane-inference) | [`2024-10-21`](/azure/ai-services/openai/reference#data-plane-inference) | [Spec files](https://github.com/Azure/azure-rest-api-specs/tree/main/specification/cognitiveservices/data-plane/AzureOpenAI/inference) | The data plane inference API provides the inference capabilities/endpoints for features like completions, chat completions, embeddings, speech/whisper, on your data, Dall-e, assistants, etc. |
+| **Data plane - authoring** | `2024-12-01-preview` | `2024-10-21` | [Spec files](https://github.com/Azure/azure-rest-api-specs/tree/main/specification/cognitiveservices/data-plane/AzureOpenAI/authoring) | The data plane authoring API controls [fine-tuning](/rest/api/azureopenai/fine-tuning?view=rest-azureopenai-2024-08-01-preview&preserve-view=true), [file-upload](/rest/api/azureopenai/files/upload?view=rest-azureopenai-2024-08-01-preview&tabs=HTTP&preserve-view=true), [ingestion jobs](/rest/api/azureopenai/ingestion-jobs/create?view=rest-azureopenai-2024-08-01-preview&tabs=HTTP&preserve-view=true), [batch](/rest/api/azureopenai/batch?view=rest-azureopenai-2024-08-01-preview&tabs=HTTP&preserve-view=true) and certain [model level queries](/rest/api/azureopenai/models/get?view=rest-azureopenai-2024-08-01-preview&tabs=HTTP&preserve-view=true)
+| **Data plane - inference** | [`2024-12-01-preview`](/azure/ai-services/openai/reference-preview#data-plane-inference) | [`2024-10-21`](/azure/ai-services/openai/reference#data-plane-inference) | [Spec files](https://github.com/Azure/azure-rest-api-specs/tree/main/specification/cognitiveservices/data-plane/AzureOpenAI/inference) | The data plane inference API provides the inference capabilities/endpoints for features like completions, chat completions, embeddings, speech/whisper, on your data, Dall-e, assistants, etc. |
 
 ## Authentication
 

@@ -1,17 +0,0 @@
----
-manager: nitinme
-author: aahill
-ms.author: aahi
-ms.service: azure-ai-openai
-ms.topic: include
-ms.date: 10/09/2024
-ms.custom: devex-track-js, devex-track-typescript
----
-|Variable name | Value |
-|--------------------------|-------------|
-| `AZURE_OPENAI_ENDPOINT`               | This value can be found in the **Keys and Endpoint** section when examining your resource from the Azure portal. |
-| `AZURE_OPENAI_API_KEY` | This value can be found in the **Keys and Endpoint** section when examining your resource from the Azure portal. You can use either `KEY1` or `KEY2`.|
-| `AZURE_OPENAI_DEPLOYMENT_NAME` | This value will correspond to the custom name you chose for your deployment when you deployed a model. This value can be found under **Resource Management** > **Model Deployments** in the Azure portal.|
-| `OPENAI_API_VERSION`|Learn more about [API Versions](/azure/ai-services/openai/api-version-deprecation).|
-
-Learn more about [finding API keys](/azure/ai-services/cognitive-services-environment-variables) and [setting environment variables](/azure/ai-services/cognitive-services-environment-variables).

@@ -1,16 +0,0 @@
----
-manager: nitinme
-author: aahill
-ms.author: aahi
-ms.service: azure-ai-openai
-ms.topic: include
-ms.date: 10/0//2024
-ms.custom: devex-track-js, devex-track-typescript
----
-|Variable name | Value |
-|--------------------------|-------------|
-| `AZURE_OPENAI_ENDPOINT`               | This value can be found in the **Keys and Endpoint** section when examining your resource from the Azure portal. |
-| `AZURE_OPENAI_DEPLOYMENT_NAME` | This value will correspond to the custom name you chose for your deployment when you deployed a model. This value can be found under **Resource Management** > **Model Deployments** in the Azure portal.|
-| `OPENAI_API_VERSION`|Learn more about [API Versions](/azure/ai-services/openai/api-version-deprecation).|
-
-Learn more about [keyless authentication](/azure/ai-services/authentication) and [setting environment variables](/azure/ai-services/cognitive-services-environment-variables).
\ No newline at end of file

@@ -22,13 +22,11 @@ ms.custom: passwordless-ts, devex-track-js
 - We recommend reviewing the [Responsible AI transparency note](/legal/cognitive-services/openai/transparency-note?context=%2Fazure%2Fai-services%2Fopenai%2Fcontext%2Fcontext&tabs=text) and other [Responsible AI resources](/legal/cognitive-services/openai/overview?context=%2Fazure%2Fai-services%2Fopenai%2Fcontext%2Fcontext) to familiarize yourself with the capabilities and limitations of the Azure OpenAI Service.
 - An Azure OpenAI resource with the `gpt-4 (1106-preview)` model deployed was used testing this example. 
 
-## Microsoft Entra ID authentication is recommended
+### Microsoft Entra ID prerequisites
 
-For _keyless_ authentication, you need to 
-
-1. Use the `@azure/identity` package.
-1. Assign the `Cognitive Services User` role to your user account. This can be done in the Azure portal under **Access control (IAM)** > **Add role assignment**.
-1. Sign in with the Azure CLI such as `az login`.
+For the recommended keyless authentication with Microsoft Entra ID, you need to:
+- Install the [Azure CLI](/cli/azure/install-azure-cli) used for keyless authentication with Microsoft Entra ID.
+- Assign the `Cognitive Services User` role to your user account. You can assign roles in the Azure portal under **Access control (IAM)** > **Add role assignment**.
 
 ## Set up
 
@@ -64,18 +62,10 @@ For _keyless_ authentication, you need to
     npm install @azure/identity
     ```
 
-## Retrieve resource information
-
-#### [Microsoft Entra ID](#tab/javascript-keyless)
-
-[!INCLUDE [assistants-keyless-environment-variables](assistants-env-var-without-key.md)]
 
+## Retrieve resource information
 
-#### [API key](#tab/javascript-key)
-
-[!INCLUDE [assistants-key-environment-variables](assistants-env-var-key.md)]
-
----
+[!INCLUDE [resource authentication](resource-auth.md)]
 
 > [!CAUTION]
 > To use the recommended keyless authentication with the SDK, make sure that the `AZURE_OPENAI_API_KEY` environment variable isn't set. 
@@ -98,7 +88,7 @@ An individual assistant can access up to 128 tools including `code interpreter`,
     
 ## Create a new JavaScript application
 
-#### [Microsoft Entra ID](#tab/javascript-keyless)
+#### [Microsoft Entra ID](#tab/keyless)
 
 1. Create the `index.js` file with the following code:
 
@@ -205,7 +195,7 @@ An individual assistant can access up to 128 tools including `code interpreter`,
 
 
 
-#### [API key](#tab/javascript-key)
+#### [API key](#tab/api-key)
 
 1. Create the `index.js` file with the following code:
 

@@ -24,7 +24,7 @@ ms.date: 05/22/2024
 
 ## Passwordless authentication is recommended
 
-For passwordless authentication, you need to 
+For passwordless authentication, you need to:
 
 1. Use the [azure-identity](https://pypi.org/project/azure-identity/) package.
 2. Assign the `Cognitive Services User` role to your user account. This can be done in the Azure portal under **Access control (IAM)** > **Add role assignment**.

@@ -1,126 +0,0 @@
----
-title: 'Quickstart: Use Azure OpenAI Assistants (Preview) via the Azure OpenAI Studio'
-titleSuffix: Azure OpenAI
-description: Walkthrough on how to get started with Azure OpenAI and make your first completions call with Azure OpenAI Studio. 
-manager: nitinme
-author: aahill
-ms.author: aahi
-ms.service: azure-ai-openai
-ms.topic: include
-ms.date: 05/31/2024
----
-
-## Prerequisites
-
-- An Azure subscription - <a href="https://azure.microsoft.com/free/cognitive-services" target="_blank">Create one for free</a>.
-- An Azure OpenAI resource with a [compatible model in a supported region](../concepts/models.md#assistants-preview).
-- We recommend reviewing the [Responsible AI transparency note](/legal/cognitive-services/openai/transparency-note?context=%2Fazure%2Fai-services%2Fopenai%2Fcontext%2Fcontext&tabs=text) and other [Responsible AI resources](/legal/cognitive-services/openai/overview?context=%2Fazure%2Fai-services%2Fopenai%2Fcontext%2Fcontext) to familiarize yourself with the capabilities and limitations of the Azure OpenAI Service.
-
-## Go to the Azure OpenAI Studio
-
-Navigate to Azure OpenAI Studio at <a href="https://oai.azure.com/" target="_blank">https://oai.azure.com/</a> and sign-in with credentials that have access to your Azure OpenAI resource. During or after the sign-in workflow, select the appropriate directory, Azure subscription, and Azure OpenAI resource.
-
-From the Azure OpenAI Studio landing page launch the Assistant's playground from the left-hand navigation **Playground** > **Assistants (Preview)**
-
-:::image type="content" source="../media/quickstarts/assistants-studio.png" alt-text="Screenshot of the Azure OpenAI Studio landing page." lightbox="../media/quickstarts/assistants-studio.png":::
-
-## Playground
-
-The Assistants playground allows you to explore, prototype, and test AI Assistants without needing to run any code. From this page, you can quickly iterate and experiment with new ideas.
-
-:::image type="content" source="../media/quickstarts/assistants-playground.png" alt-text="Screenshot of the Assistant configuration screen without all the values filled in." lightbox="../media/quickstarts/assistants-playground.png":::
-
-### Assistant setup
-
-Use the **Assistant setup** pane to create a new AI assistant or to select an existing assistant. 
-
-| **Name** | **Description** |
-|:---|:---|
-| **Assistant name** | Your deployment name that is associated with a specific model. |
-| **Instructions** | Instructions are similar to system messages this is where you give the model guidance about how it should behave and any context it should reference when generating a response. You can describe the assistant's personality, tell it what it should and shouldn't answer, and tell it how to format responses. You can also provide examples of the steps it should take when answering responses. |
-| **Deployment** | This is where you set which model deployment to use with your assistant. |
-| **Functions**| Create custom function definitions for the models to formulate API calls and structure data outputs based on your specifications |
-| **Code interpreter** | Code interpreter provides access to a sandboxed Python environment that can be used to allow the model to test and execute code. |
-| **Files** | You can upload up to 20 files, with a max file size of 512 MB to use with tools. You can upload up to 10,000 files using [Azure AI Foundry portal](../assistants-quickstart.md?pivots=ai-foundry-portal). |
-
-### Tools
-
-An individual assistant can access up to 128 tools including `code interpreter`, as well as any custom tools you create via [functions](../how-to/assistant-functions.md).
-
-### Chat session
-
-Chat session also known as a *thread* within the Assistant's API is where the conversation between the user and assistant occurs. Unlike traditional chat completion calls there is no limit to the number of messages in a thread. The assistant will automatically compress requests to fit the input token limit of the model.
-
-This also means that you are not controlling how many tokens are passed to the model during each turn of the conversation. Managing tokens is abstracted away and handled entirely by the Assistants API.
-
-Select the **Clear chat** button to delete the current conversation history.
-
-Underneath the text input box there are two buttons:
-
-- Add a message without run.
-- Add and run.
-
-### Logs
-
-Logs provide a detailed snapshot of what the assistant API activity.
-
-### Show panels
-
-By default there are three panels: assistant setup, chat session, and Logs. **Show panels** allows you to add, remove, and rearrange the panels. If you ever close a panel and need to get it back, use **Show panels** to restore the lost panel.
-
-## Create your first assistant
-
-1. From the assistant setup drop-down, select **New**
-2. Give your Assistant a name
-3. Enter the following instructions "You are an AI assistant that can write code to help answer math questions"
-4. Select a deployment of `gpt-4 (1106-preview)`. The dropdown will display your custom deployment names so if your model/deployment names are different you should choose a deployment where the underlying model is equivalent to the 1106-preview model.
-5. Select the toggle enabling code interpreter.
-6. Select Save.
-
-    :::image type="content" source="../media/quickstarts/assistant-configuration.png" alt-text="Screenshot of the assistant with configuration details entered." lightbox="../media/quickstarts/assistant-configuration.png":::
-
-7. Enter a question for the assistant to answer: "I need to solve the equation `3x + 11 = 14`. Can you help me?"
-8. Select the **Add and run button** :::image type="icon" source="../media/quickstarts/run.png":::
-
-    ```output
-    The solution to the equation (3x + 11 = 14) is (x = 1).
-    ```
-
-    While we can see that answer is correct, to confirm that the model used code interpreter to get to this answer, and that the code it wrote is valid rather than just repeating an answer from the model's training data we'll ask another question.
-
-9. Enter the follow-up question: "Show me the code you ran to get this solution."
-
-    ```output
-    Certainly! Here's the Python code I ran to find the solution for the equation (3x + 11 = 14):
-    ```
-
-    ```python
-    from sympy import symbols, Eq, solve  
-      
-    # Define the variable  
-    x = symbols('x')  
-      
-    # Define the equation  
-    equation = Eq(3*x + 11, 14)  
-      
-    # Solve the equation  
-    solution = solve(equation, x)  
-    solution  
-    ```
-
-:::image type="content" source="../media/quickstarts/assistant-session.png" alt-text="Screenshot of conversation session in the Assistant playground." lightbox="../media/quickstarts/assistant-session.png":::
-
-You could also consult the logs in the right-hand panel to confirm that code interpreter was used and to validate the code that was run to generate the response. It is important to remember that while code interpreter gives the model the capability to respond to more complex math questions by converting the questions into code and running in a sandboxed Python environment, you still need to validate the response to confirm that the model correctly translated your question into a valid representation in code.
-
-## Clean up resources
-
-If you want to clean up and remove an Azure OpenAI resource, you can delete the resource or resource group. Deleting the resource group also deletes any other resources associated with it.
-
-- [Azure portal](../../multi-service-resource.md?pivots=azportal#clean-up-resources)
-- [Azure CLI](../../multi-service-resource.md?pivots=azcli#clean-up-resources)
-
-## See also
-
-* Learn more about how to use Assistants with our [How-to guide on Assistants](../how-to/assistant.md).
-* [Azure OpenAI Assistants API samples](https://github.com/Azure-Samples/azureai-samples/tree/main/scenarios/Assistants)
-

@@ -23,13 +23,11 @@ ms.custom: passwordless-js, devex-track-typescript
 - We recommend reviewing the [Responsible AI transparency note](/legal/cognitive-services/openai/transparency-note?context=%2Fazure%2Fai-services%2Fopenai%2Fcontext%2Fcontext&tabs=text) and other [Responsible AI resources](/legal/cognitive-services/openai/overview?context=%2Fazure%2Fai-services%2Fopenai%2Fcontext%2Fcontext) to familiarize yourself with the capabilities and limitations of the Azure OpenAI Service.
 - An Azure OpenAI resource with the `gpt-4 (1106-preview)` model deployed was used testing this example. 
 
-## Passwordless authentication is recommended
+### Microsoft Entra ID prerequisites
 
-For passwordless authentication, you need to 
-
-1. Use the `@azure/identity` package.
-1. Assign the `Cognitive Services User` role to your user account. This can be done in the Azure portal under **Access control (IAM)** > **Add role assignment**.
-1. Sign in with the Azure CLI such as `az login`.
+For the recommended keyless authentication with Microsoft Entra ID, you need to:
+- Install the [Azure CLI](/cli/azure/install-azure-cli) used for keyless authentication with Microsoft Entra ID.
+- Assign the `Cognitive Services User` role to your user account. You can assign roles in the Azure portal under **Access control (IAM)** > **Add role assignment**.
 
 ## Set up
 
@@ -67,22 +65,11 @@ For passwordless authentication, you need to
 
 ## Retrieve resource information
 
-
-#### [Microsoft Entra ID](#tab/typescript-keyless)
-
-[!INCLUDE [assistants-keyless-environment-variables](assistants-env-var-without-key.md)]
-
-
-#### [API key](#tab/typescript-key)
-
-[!INCLUDE [assistants-key-environment-variables](assistants-env-var-key.md)]
-
----
+[!INCLUDE [resource authentication](resource-auth.md)]
 
 > [!CAUTION]
 > To use the recommended keyless authentication with the SDK, make sure that the `AZURE_OPENAI_API_KEY` environment variable isn't set. 
 
-
 ## Create an assistant
 
 In our code we're going to specify the following values:

@@ -14,7 +14,7 @@ ms.date: 10/22
 [Source code](https://github.com/openai/openai-node) | [Package (npm)](https://www.npmjs.com/package/openai) | [Samples](https://github.com/Azure/azure-sdk-for-js/tree/main/sdk/openai/openai/samples)
 
 > [!NOTE]
-> This article has been updated to use the [latest OpenAI npm package](https://www.npmjs.com/package/openai) which now fully supports Azure OpenAI. If you are looking for code examples for the legacy Azure OpenAI JavaScript SDK they are currently still [available in this repo](https://github.com/Azure/azure-sdk-for-js/tree/main/sdk/openai/openai/samples/v2-beta/javascript).
+> This guide uses the [latest OpenAI npm package](https://www.npmjs.com/package/openai) which now fully supports Azure OpenAI. If you're looking for code examples for the legacy Azure OpenAI JavaScript SDK, they're currently still [available in this repo](https://github.com/Azure/azure-sdk-for-js/tree/main/sdk/openai/openai/samples/v2-beta/javascript).
 
 ## Prerequisites
 
@@ -23,12 +23,19 @@ ms.date: 10/22
 - [Azure CLI](/cli/azure/install-azure-cli) used for passwordless authentication in a local development environment, create the necessary context by signing in with the Azure CLI.
 - An Azure OpenAI Service resource with either a `gpt-35-turbo` or `gpt-4` series models deployed. For more information about model deployment, see the [resource deployment guide](../how-to/create-resource.md).
 
+### Microsoft Entra ID prerequisites
 
-## Set up
+For the recommended keyless authentication with Microsoft Entra ID, you need to:
+- Install the [Azure CLI](/cli/azure/install-azure-cli) used for keyless authentication with Microsoft Entra ID.
+- Assign the `Cognitive Services User` role to your user account. You can assign roles in the Azure portal under **Access control (IAM)** > **Add role assignment**.
 
-[!INCLUDE [get-key-endpoint](get-key-endpoint.md)]
+## Retrieve resource information
+
+[!INCLUDE [resource authentication](resource-auth.md)]
+
+> [!CAUTION]
+> To use the recommended keyless authentication with the SDK, make sure that the `AZURE_OPENAI_API_KEY` environment variable isn't set. 
 
-[!INCLUDE [environment-variables](environment-variables.md)]
 
 ## Create a Node application
 
@@ -42,7 +49,7 @@ Install the required packages for JavaScript with npm from within the context of
 npm install openai @azure/identity
 ```
 
-Your app's _package.json_ file will be updated with the dependencies.
+Your app's _package.json_ file is updated with the dependencies.
 
 ## Create a sample application
 
@@ -158,7 +165,7 @@ node.exe ChatCompletion.js
 ---
 
 > [!NOTE]
-> If your receive the error: *Error occurred: OpenAIError: The `apiKey` and `azureADTokenProvider` arguments are mutually exclusive; only one can be passed at a time.* You may need to remove a pre-existing environment variable for the API key from your system. Even though the Microsoft Entra ID code sample is not explicitly referencing the API key environment variable, if one is present on the system executing this sample, this error will still be generated.
+> If your receive the error: *Error occurred: OpenAIError: The `apiKey` and `azureADTokenProvider` arguments are mutually exclusive; only one can be passed at a time.* You might need to remove a preexisting environment variable for the API key from your system. Even though the Microsoft Entra ID code sample isn't explicitly referencing the API key environment variable, if one is present on the system executing this sample, this error is still generated.
 
 
 ## Clean up resources

@@ -24,7 +24,7 @@ From the Azure OpenAI Studio landing page, select **Chat playground**.
 
 ## Playground
 
-Start exploring OpenAI capabilities with a no-code approach through the Azure OpenAI Studio Chat playground. From this page, you can quickly iterate and experiment with the capabilities.
+Start exploring Azure OpenAI Service capabilities with a no-code approach through the Azure OpenAI Studio Chat playground. From this page, you can quickly iterate and experiment with the capabilities.
 
 :::image type="content" source="../media/quickstarts/chatgpt-playground-load-new.png" alt-text="Screenshot of the Chat playground page." lightbox="../media/quickstarts/chatgpt-playground-load-new.png":::
 

@@ -14,24 +14,28 @@ ms.date: 10/22
 [Source code](https://github.com/openai/openai-node) | [Package (npm)](https://www.npmjs.com/package/openai) | [Samples](https://github.com/Azure/azure-sdk-for-js/tree/main/sdk/openai/openai/samples)
 
 > [!NOTE]
-> This article has been updated to use the [latest OpenAI npm package](https://www.npmjs.com/package/openai) which now fully supports Azure OpenAI. If you are looking for code examples for the legacy Azure OpenAI JavaScript SDK they are currently still [available in this repo](https://github.com/Azure/azure-sdk-for-js/tree/main/sdk/openai/openai/samples/v2-beta/javascript).
+> This guide uses the [latest OpenAI npm package](https://www.npmjs.com/package/openai) which now fully supports Azure OpenAI. If you're looking for code examples for the legacy Azure OpenAI JavaScript SDK, they're currently still [available in this repo](https://github.com/Azure/azure-sdk-for-js/tree/main/sdk/openai/openai/samples/v2-beta/javascript).
 
 ## Prerequisites
 
-
 - An Azure subscription - [Create one for free](https://azure.microsoft.com/free/cognitive-services?azure-portal=true)
 - [LTS versions of Node.js](https://github.com/nodejs/release#release-schedule)
 - [TypeScript](https://www.typescriptlang.org/download/)
 - [Azure CLI](/cli/azure/install-azure-cli) used for passwordless authentication in a local development environment, create the necessary context by signing in with the Azure CLI.
 - An Azure OpenAI Service resource with a `gpt-35-turbo` or `gpt-4` series models deployed. For more information about model deployment, see the [resource deployment guide](../how-to/create-resource.md).
 
+### Microsoft Entra ID prerequisites
 
+For the recommended keyless authentication with Microsoft Entra ID, you need to:
+- Install the [Azure CLI](/cli/azure/install-azure-cli) used for keyless authentication with Microsoft Entra ID.
+- Assign the `Cognitive Services User` role to your user account. You can assign roles in the Azure portal under **Access control (IAM)** > **Add role assignment**.
 
-## Set up
+## Retrieve resource information
 
-[!INCLUDE [get-key-endpoint](get-key-endpoint.md)]
+[!INCLUDE [resource authentication](resource-auth.md)]
 
-[!INCLUDE [environment-variables](environment-variables.md)]
+> [!CAUTION]
+> To use the recommended keyless authentication with the SDK, make sure that the `AZURE_OPENAI_API_KEY` environment variable isn't set. 
 
 ## Create a Node application
 
@@ -45,7 +49,7 @@ Install the required packages for JavaScript with npm from within the context of
 npm install openai @azure/identity
 ```
 
-Your app's _package.json_ file will be updated with the dependencies.
+Your app's _package.json_ file is updated with the dependencies.
 
 
 ## Create a sample application
@@ -219,7 +223,7 @@ node.exe ChatCompletion.js
 ---
 
 > [!NOTE]
-> If your receive the error: *Error occurred: OpenAIError: The `apiKey` and `azureADTokenProvider` arguments are mutually exclusive; only one can be passed at a time.* You may need to remove a pre-existing environment variable for the API key from your system. Even though the Microsoft Entra ID code sample is not explicitly referencing the API key environment variable, if one is present on the system executing this sample, this error will still be generated.
+> If your receive the error: *Error occurred: OpenAIError: The `apiKey` and `azureADTokenProvider` arguments are mutually exclusive; only one can be passed at a time.* You might need to remove a preexisting environment variable for the API key from your system. Even though the Microsoft Entra ID code sample isn't explicitly referencing the API key environment variable, if one is present on the system executing this sample, this error is still generated.
 
 
 ## Clean up resources

@@ -12,12 +12,12 @@ ms.date: 11/06/2024
 recommendations: false
 ---
 
-## Add your data using Azure OpenAI Studio
+## Add your data using Azure AI Foundry portal
 
 > [!TIP]
 > You can [use the Azure Developer CLI](../how-to/azure-developer-cli.md) to programmatically create the resources needed for Azure OpenAI On Your Data 
 
-Navigate to [Azure AI Foundry](https://ai.azure.com/) and sign-in with credentials that have access to your Azure OpenAI resource. 
+Navigate to [Azure AI Foundry portal](https://ai.azure.com/) and sign-in with credentials that have access to your Azure OpenAI resource. 
 
 1. You can either [create an Azure AI Foundry project](../../../ai-studio/how-to/create-projects.md) by clicking **Create project**, or continue directly by clicking the button on the **Focused on Azure OpenAI Service** tile.  
 
@@ -41,7 +41,7 @@ Navigate to [Azure AI Foundry](https://ai.azure.com/) and sign-in with credentia
 
     1. Select your Azure AI Search resource, and select the acknowledgment that connecting it will incur usage on your account. Then select **Next**.
 
-    :::image type="content" source="../media/quickstarts/add-your-data-source.png" alt-text="A screenshot showing options for selecting a data source in Azure OpenAI Studio." lightbox="../media/quickstarts/add-your-data-source.png":::
+    :::image type="content" source="../media/quickstarts/add-your-data-source.png" alt-text="A screenshot showing options for selecting a data source in Azure AI Foundry portal." lightbox="../media/quickstarts/add-your-data-source.png":::
 
 
 1. On the **Upload files** pane, select **Browse for a file** and select the files you downloaded from the [prerequisites](#prerequisites) section, or your own data. Then select **Upload files**. Then select **Next**.

@@ -97,7 +97,7 @@ Before you can generate text or inference, you need to deploy a model. You can s
 
 To deploy a model, follow these steps:
 
-1. Sign in to [Azure AI Foundry](https://oai.azure.com).
+1. Sign in to [Azure AI Foundry portal](https://ai.azure.com).
 
 2. Choose the subscription and the Azure OpenAI resource to work with, and select **Use resource**.
 

@@ -17,19 +17,23 @@ Use this guide to get started generating images with the Azure OpenAI SDK for Ja
 
 ## Prerequisites
 
-
 - An Azure subscription - [Create one for free](https://azure.microsoft.com/free/cognitive-services?azure-portal=true)
 - [LTS versions of Node.js](https://github.com/nodejs/release#release-schedule)
 - [Azure CLI](/cli/azure/install-azure-cli) used for passwordless authentication in a local development environment, create the necessary context by signing in with the Azure CLI.
 - An Azure OpenAI resource created in a supported region (see [Region availability](/azure/ai-services/openai/concepts/models#model-summary-table-and-region-availability)). For more information, see [Create a resource and deploy a model with Azure OpenAI](../how-to/create-resource.md).
 
+### Microsoft Entra ID prerequisites
 
-## Setup
+For the recommended keyless authentication with Microsoft Entra ID, you need to:
+- Install the [Azure CLI](/cli/azure/install-azure-cli) used for keyless authentication with Microsoft Entra ID.
+- Assign the `Cognitive Services User` role to your user account. You can assign roles in the Azure portal under **Access control (IAM)** > **Add role assignment**.
 
-[!INCLUDE [get-key-endpoint](get-key-endpoint.md)]
+## Retrieve resource information
 
-[!INCLUDE [environment-variables](environment-variables.md)]
+[!INCLUDE [resource authentication](resource-auth.md)]
 
+> [!CAUTION]
+> To use the recommended keyless authentication with the SDK, make sure that the `AZURE_OPENAI_API_KEY` environment variable isn't set. 
 
 ## Create a Node application
 

@@ -21,7 +21,7 @@ Use this guide to get started generating images with Azure OpenAI in your browse
 
 ## Go to Azure AI Foundry
 
-Browse to [Azure AI Foundry](https://oai.azure.com/) and sign in with the credentials associated with your Azure OpenAI resource. During or after the sign-in workflow, select the appropriate directory, Azure subscription, and Azure OpenAI resource.
+Browse to [Azure AI Foundry](https://ai.azure.com/) and sign in with the credentials associated with your Azure OpenAI resource. During or after the sign-in workflow, select the appropriate directory, Azure subscription, and Azure OpenAI resource.
 
 From the Azure AI Foundry landing page, create or select a new project. Navigate to the **Models + endpoints** page on the left nav. Select **Deploy model** and then choose one of the DALL-E models from the list. Complete the deployment process. 
 

@@ -17,21 +17,24 @@ Use this guide to get started generating images with the Azure OpenAI SDK for Ja
 
 ## Prerequisites
 
-
 - An Azure subscription - [Create one for free](https://azure.microsoft.com/free/cognitive-services?azure-portal=true)
 - [LTS versions of Node.js](https://github.com/nodejs/release#release-schedule)
 - [TypeScript](https://www.typescriptlang.org/download/)
 - [Azure CLI](/cli/azure/install-azure-cli) used for passwordless authentication in a local development environment, create the necessary context by signing in with the Azure CLI.
 - An Azure OpenAI resource created in a supported region (see [Region availability](/azure/ai-services/openai/concepts/models#model-summary-table-and-region-availability)). For more information, see [Create a resource and deploy a model with Azure OpenAI](../how-to/create-resource.md).
 
+### Microsoft Entra ID prerequisites
 
+For the recommended keyless authentication with Microsoft Entra ID, you need to:
+- Install the [Azure CLI](/cli/azure/install-azure-cli) used for keyless authentication with Microsoft Entra ID.
+- Assign the `Cognitive Services User` role to your user account. You can assign roles in the Azure portal under **Access control (IAM)** > **Add role assignment**.
 
-## Setup
-
-[!INCLUDE [get-key-endpoint](get-key-endpoint.md)]
+## Retrieve resource information
 
-[!INCLUDE [environment-variables](environment-variables.md)]
+[!INCLUDE [resource authentication](resource-auth.md)]
 
+> [!CAUTION]
+> To use the recommended keyless authentication with the SDK, make sure that the `AZURE_OPENAI_API_KEY` environment variable isn't set. 
 
 ## Create a Node application
 

@@ -10,10 +10,9 @@ ms.date: 02/09/2024
 
 ## Deploy your model
 
-Once you're satisfied with the experience in Azure OpenAI studio, you can deploy a web app directly from the 
-Studio by selecting the **Deploy to** button. 
+Once you're satisfied with the experience, you can deploy a web app directly from the portal by selecting the **Deploy to** button. 
 
-:::image type="content" source="../media/use-your-data/deploy-model.png" alt-text="A screenshot showing the model deployment button in Azure OpenAI Studio." lightbox="../media/use-your-data/deploy-model.png":::
+:::image type="content" source="../media/use-your-data/deploy-model.png" alt-text="A screenshot showing the model deployment button in the portal." lightbox="../media/use-your-data/deploy-model.png":::
 
 This gives you the option to either deploy to a standalone web application, or a copilot in Copilot Studio (preview) if you're [using your own data](../concepts/use-your-data.md#deploy-to-a-copilot-preview-teams-app-preview-or-web-app) on the model. 
 

@@ -1,15 +0,0 @@
----
-author: eric-urban 
-ms.author: eur 
-ms.service: azure-ai-openai
-ms.topic: include
-ms.date: 12/27/2024
----
-
-|Variable name | Value |
-|--------------------------|-------------|
-| `AZURE_OPENAI_ENDPOINT`               | This value can be found in the **Keys and Endpoint** section when examining your resource from the Azure portal. |
-| `AZURE_OPENAI_DEPLOYMENT_NAME` | This value will correspond to the custom name you chose for your deployment when you deployed a model. This value can be found under **Resource Management** > **Model Deployments** in the Azure portal.|
-| `OPENAI_API_VERSION`|Learn more about [API Versions](/azure/ai-services/openai/api-version-deprecation).|
-
-Learn more about [keyless authentication](/azure/ai-services/authentication) and [setting environment variables](/azure/ai-services/cognitive-services-environment-variables).
\ No newline at end of file

@@ -306,4 +306,4 @@ You can delete a fine-tuned model on the **Fine-tuning** page in Azure AI Foundr
 
 ### Delete your training files
 
-You can optionally delete training and validation files that you uploaded for training, and result files generated during training. For this you need to go to Azure OpenAI Studio and navigate to the **Management** > **Data files** pane. Select the file to delete, and then select **Delete** to delete the file.
+You can optionally delete training and validation files that you uploaded for training, and result files generated during training. For this you need to go to Azure AI Foundry portal and navigate to the **Management** > **Data + indexes** pane. Select the file to delete, and then select **Delete** to delete the file.

@@ -346,7 +346,7 @@ To examine the individual fine-tuning events that were generated during training
 
 # [OpenAI Python 1.x](#tab/python-new)
 
-You may need to upgrade your OpenAI client library to the latest version with `pip install openai --upgrade` to run this command.
+You might need to upgrade your OpenAI client library to the latest version with `pip install openai --upgrade` to run this command.
 
 ```python
 response = client.fine_tuning.jobs.list_events(fine_tuning_job_id=job_id, limit=10)
@@ -367,7 +367,7 @@ You can run the list checkpoints command to retrieve the list of checkpoints ass
 
 # [OpenAI Python 1.x](#tab/python-new)
 
-You may need to upgrade your OpenAI client library to the latest version with `pip install openai --upgrade` to run this command.
+You might need to upgrade your OpenAI client library to the latest version with `pip install openai --upgrade` to run this command.
 
 ```python
 response = client.fine_tuning.jobs.list_events(fine_tuning_job_id=job_id, limit=10)

@@ -1,5 +1,5 @@
 ---
-title: 'Customize a model with Azure OpenAI Service and Azure AI Foundry'
+title: 'Customize a model with Azure OpenAI Service and Azure AI Foundry portal'
 titleSuffix: Azure OpenAI
 description: Learn how to create your own custom model with Azure OpenAI Service by using the Azure AI Foundry portal.
 #services: cognitive-services
@@ -40,9 +40,9 @@ Or you can fine tune a previously fine-tuned model, formatted as base-model.ft-{
 Consult the [models page](../concepts/models.md#fine-tuning-models) to check which regions currently support fine-tuning.
 
 
-## Review the workflow for Azure AI Foundry
+## Review the workflow for Azure AI Foundry portal
 
-Take a moment to review the fine-tuning workflow for using Azure AI Foundry:
+Take a moment to review the fine-tuning workflow for using Azure AI Foundry portal:
 
 1. Prepare your training and validation data.
 1. Use the **Create custom model** wizard in Azure AI Foundry portal to train your custom model.
@@ -150,9 +150,9 @@ After it guides you through the process of implementing suggested changes, the t
 
 ## Use the Create custom model wizard
 
-Azure AI Foundry provides the **Create custom model** wizard, so you can interactively create and train a fine-tuned model for your Azure resource.
+Azure AI Foundry portal provides the **Create custom model** wizard, so you can interactively create and train a fine-tuned model for your Azure resource.
 
-1. Open Azure AI Foundry at <a href="https://oai.azure.com/" target="_blank">https://oai.azure.com/</a> and sign in with credentials that have access to your Azure OpenAI resource. During the sign-in workflow, select the appropriate directory, Azure subscription, and Azure OpenAI resource.
+1. Open Azure AI Foundry portal at <a href="https://oai.azure.com/" target="_blank">https://oai.azure.com/</a> and sign in with credentials that have access to your Azure OpenAI resource. During the sign-in workflow, select the appropriate directory, Azure subscription, and Azure OpenAI resource.
 
 1. In Azure AI Foundry portal, browse to the **Tools > Fine-tuning** pane, and select **Fine-tune model**.
 
@@ -310,7 +310,7 @@ If you're ready to train your model, select **Start Training job** to start the
 
 The **Models** pane displays information about your custom model in the **Customized models** tab. The tab includes information about the status and job ID of the fine-tune job for your custom model. When the job completes, the tab displays the file ID of the result file. You might need to select **Refresh** in order to see an updated status for the model training job.
 
-:::image type="content" source="../media/fine-tuning/studio-models-job-running.png" alt-text="Screenshot of the Models pane from Azure AI Foundry, with a custom model displayed." lightbox="../media/fine-tuning/studio-models-job-running.png":::
+:::image type="content" source="../media/fine-tuning/studio-models-job-running.png" alt-text="Screenshot of the Models pane from Azure AI Foundry portal, with a custom model displayed." lightbox="../media/fine-tuning/studio-models-job-running.png":::
 
 After you start a fine-tuning job, it can take some time to complete. Your job might be queued behind other jobs on the system. Training your model can take minutes or hours depending on the model and dataset size.
 
@@ -366,13 +366,13 @@ Cross subscription/region deployment can be accomplished via [Python](/azure/ai-
 
 ## Use a deployed custom model
 
-After your custom model deploys, you can use it like any other deployed model. You can use the **Playgrounds** in [Azure AI Foundry](https://oai.azure.com) to experiment with your new deployment. You can continue to use the same parameters with your custom model, such as `temperature` and `max_tokens`, as you can with other deployed models. For fine-tuned `babbage-002` and `davinci-002` models you will use the Completions playground and the Completions API. For fine-tuned `gpt-35-turbo-0613` models you will use the Chat playground and the Chat completion API.
+After your custom model deploys, you can use it like any other deployed model. You can use the **Playgrounds** in [Azure AI Foundry portal](https://oai.azure.com) to experiment with your new deployment. You can continue to use the same parameters with your custom model, such as `temperature` and `max_tokens`, as you can with other deployed models. For fine-tuned `babbage-002` and `davinci-002` models you will use the Completions playground and the Completions API. For fine-tuned `gpt-35-turbo-0613` models you will use the Chat playground and the Chat completion API.
 
 :::image type="content" source="../media/quickstarts/playground-load-new.png" alt-text="Screenshot of the Playground pane in Azure AI Foundry portal, with sections highlighted." lightbox="../media/quickstarts/playground-load-new.png":::
 
 ## Analyze your custom model
 
-Azure OpenAI attaches a result file named _results.csv_ to each fine-tuning job after it completes. You can use the result file to analyze the training and validation performance of your custom model. The file ID for the result file is listed for each custom model in the **Result file Id** column on the **Models** pane for Azure AI Foundry. You can use the file ID to identify and download the result file from the **Data files** pane of Azure AI Foundry.
+Azure OpenAI attaches a result file named _results.csv_ to each fine-tuning job after it completes. You can use the result file to analyze the training and validation performance of your custom model. The file ID for the result file is listed for each custom model in the **Result file Id** column on the **Models** pane for Azure AI Foundry portal. You can use the file ID to identify and download the result file from the **Data files** pane of Azure AI Foundry portal.
 
 The result file is a CSV file that contains a header row and a row for each training step performed by the fine-tuning job. The result file contains the following columns:
 
@@ -409,7 +409,7 @@ You can delete a custom model on the **Models** pane in Azure AI Foundry portal.
 
 ### Delete your training files
 
-You can optionally delete training and validation files that you uploaded for training, and result files generated during training, on the **Management** > **Data files** pane in Azure AI Foundry portal. Select the file to delete, and then select **Delete** to delete the file.
+You can optionally delete training and validation files that you uploaded for training, and result files generated during training, on the **Management** > **Data + indexes** pane in Azure AI Foundry portal. Select the file to delete, and then select **Delete** to delete the file.
 
 ## Continuous fine-tuning
 

@@ -23,16 +23,21 @@ This SDK is provided by OpenAI with Azure specific types provided by Azure.
 - [Azure CLI](/cli/azure/install-azure-cli) used for passwordless authentication in a local development environment, create the necessary context by signing in with the Azure CLI.
 - An Azure OpenAI resource created in a supported region (see [Region availability](/azure/ai-services/openai/concepts/models#model-summary-table-and-region-availability)). For more information, see [Create a resource and deploy a model with Azure OpenAI](../how-to/create-resource.md).
 
----
-
-
 > [!NOTE]
 > This library is maintained by OpenAI. Refer to the [release history](https://github.com/openai/openai-node/releases) to track the latest updates to the library.
 
-[!INCLUDE [get-key-endpoint](get-key-endpoint.md)]
+### Microsoft Entra ID prerequisites
+
+For the recommended keyless authentication with Microsoft Entra ID, you need to:
+- Install the [Azure CLI](/cli/azure/install-azure-cli) used for keyless authentication with Microsoft Entra ID.
+- Assign the `Cognitive Services User` role to your user account. You can assign roles in the Azure portal under **Access control (IAM)** > **Add role assignment**.
+
+## Retrieve resource information
 
-[!INCLUDE [environment-variables](environment-variables.md)]
+[!INCLUDE [resource authentication](resource-auth.md)]
 
+> [!CAUTION]
+> To use the recommended keyless authentication with the SDK, make sure that the `AZURE_OPENAI_API_KEY` environment variable isn't set. 
 
 ## Create a Node application
 

@@ -24,14 +24,21 @@ This SDK is provided by OpenAI with Azure specific types provided by Azure.
 - [Azure CLI](/cli/azure/install-azure-cli) used for passwordless authentication in a local development environment, create the necessary context by signing in with the Azure CLI.
 - An Azure OpenAI resource created in a supported region (see [Region availability](/azure/ai-services/openai/concepts/models#model-summary-table-and-region-availability)). For more information, see [Create a resource and deploy a model with Azure OpenAI](../how-to/create-resource.md).
 
-
 > [!NOTE]
 > This library is maintained by OpenAI. Refer to the [release history](https://github.com/openai/openai-node/releases) to track the latest updates to the library.
 
-[!INCLUDE [get-key-endpoint](get-key-endpoint.md)]
+### Microsoft Entra ID prerequisites
+
+For the recommended keyless authentication with Microsoft Entra ID, you need to:
+- Install the [Azure CLI](/cli/azure/install-azure-cli) used for keyless authentication with Microsoft Entra ID.
+- Assign the `Cognitive Services User` role to your user account. You can assign roles in the Azure portal under **Access control (IAM)** > **Add role assignment**.
+
+## Retrieve resource information
 
-[!INCLUDE [environment-variables](environment-variables.md)]
+[!INCLUDE [resource authentication](resource-auth.md)]
 
+> [!CAUTION]
+> To use the recommended keyless authentication with the SDK, make sure that the `AZURE_OPENAI_API_KEY` environment variable isn't set. 
 
 ## Create a Node application
 

@@ -14,7 +14,7 @@ ms.date: 10/22/2024
 [Source code](https://github.com/openai/openai-node) | [Package (npm)](https://www.npmjs.com/package/openai) | [Samples](https://github.com/Azure/azure-sdk-for-js/tree/main/sdk/openai/openai/samples)
 
 > [!NOTE]
-> This article has been updated to use the [latest OpenAI npm package](https://www.npmjs.com/package/openai) which now fully supports Azure OpenAI. If you are looking for code examples for the legacy Azure OpenAI JavaScript SDK they are currently still [available in this repo](https://github.com/Azure/azure-sdk-for-js/tree/main/sdk/openai/openai/samples/v2-beta/javascript).
+> This guide uses the [latest OpenAI npm package](https://www.npmjs.com/package/openai) which now fully supports Azure OpenAI. If you're looking for code examples for the legacy Azure OpenAI JavaScript SDK, they're currently still [available in this repo](https://github.com/Azure/azure-sdk-for-js/tree/main/sdk/openai/openai/samples/v2-beta/javascript).
 
 ## Prerequisites
 
@@ -24,25 +24,24 @@ ms.date: 10/22/2024
 - [Azure CLI](/cli/azure/install-azure-cli) used for passwordless authentication in a local development environment, create the necessary context by signing in with the Azure CLI.
 - An Azure OpenAI Service resource with the `gpt-35-turbo-instruct` model deployed. For more information about model deployment, see the [resource deployment guide](../how-to/create-resource.md).
 
+## Retrieve resource information
 
+[!INCLUDE [resource authentication](resource-auth.md)]
 
-## Set up
+> [!CAUTION]
+> To use the recommended keyless authentication with the SDK, make sure that the `AZURE_OPENAI_API_KEY` environment variable isn't set. 
 
-[!INCLUDE [get-key-endpoint](get-key-endpoint.md)]
-
-[!INCLUDE [environment-variables](environment-variables.md)]
+## Install the client library
 
 In a console window (such as cmd, PowerShell, or Bash), create a new directory for your app, and navigate to it.
 
-## Install the client library
-
 Install the required packages for JavaScript with npm from within the context of your new directory:
 
 ```console
 npm install openai @azure/identity
 ```
 
-Your app's _package.json_ file will be updated with the dependencies.
+Your app's _package.json_ file is updated with the dependencies.
 
 
 ## Create a sample application
@@ -146,7 +145,7 @@ Microsoft was founded on April 4, 1975.
 ```
 
 > [!NOTE]
-> If your receive the error: *Error occurred: OpenAIError: The `apiKey` and `azureADTokenProvider` arguments are mutually exclusive; only one can be passed at a time.* You may need to remove a pre-existing environment variable for the API key from your system. Even though the Microsoft Entra ID code sample is not explicitly referencing the API key environment variable, if one is present on the system executing this sample, this error will still be generated.
+> If your receive the error: *Error occurred: OpenAIError: The `apiKey` and `azureADTokenProvider` arguments are mutually exclusive; only one can be passed at a time.* You might need to remove a preexisting environment variable for the API key from your system. Even though the Microsoft Entra ID code sample isn't explicitly referencing the API key environment variable, if one is present on the system executing this sample, this error is still generated.
 
 
 ## Clean up resources

@@ -11,8 +11,7 @@ ms.date: 03/27/2024
 
 You need to select how you want to authenticate the connection from Azure OpenAI, Azure AI Search, and Azure blob storage. You can choose a *System assigned managed identity* or an *API key*. By selecting *API key* as the authentication type, the system will automatically populate the API key for you to connect with your Azure AI Search, Azure OpenAI, and Azure Blob Storage resources. By selecting *System assigned managed identity*, the authentication will be based on the [role assignment](../how-to/on-your-data-configuration.md#role-assignments) you have. *System assigned managed identity* is selected by default for security. 
 
-
-:::image type="content" source="../media/use-your-data/data-connection-authentication.png" alt-text="A screenshot showing the managed identity option in Azure OpenAI Studio." lightbox="../media/use-your-data/data-connection-authentication.png":::
+:::image type="content" source="../media/use-your-data/data-connection-authentication.png" alt-text="A screenshot showing the managed identity option in Azure AI Foundry portal." lightbox="../media/use-your-data/data-connection-authentication.png":::
 
 Once you select the **next** button, it will automatically validate your setup to use the selected authentication method. If you encounter an error, see the [role assignments article](../how-to/on-your-data-configuration.md#role-assignments) to update your setup.
 

@@ -14,7 +14,7 @@ ms.date: 12/26/2024
 - An Azure OpenAI resource created in the East US 2 or Sweden Central regions. See [Region availability](/azure/ai-services/openai/concepts/models#model-summary-table-and-region-availability).
 - Then, you need to deploy a `gpt-4o-realtime-preview` model with your Azure OpenAI resource. For more information, see [Create a resource and deploy a model with Azure OpenAI](../how-to/create-resource.md). 
 
-## Microsoft Entra ID prerequisites
+### Microsoft Entra ID prerequisites
 
 For the recommended keyless authentication with Microsoft Entra ID, you need to:
 - Install the [Azure CLI](/cli/azure/install-azure-cli) used for keyless authentication with Microsoft Entra ID.
@@ -59,22 +59,14 @@ For the recommended keyless authentication with Microsoft Entra ID, you need to:
 
 ## Retrieve resource information
 
-#### [Microsoft Entra ID](#tab/javascript-keyless)
-
-[!INCLUDE [keyless-environment-variables](env-var-without-key.md)]
-
-#### [API key](#tab/javascript-key)
-
-[!INCLUDE [key-environment-variables](env-var-key.md)]
-
----
+[!INCLUDE [resource authentication](resource-auth.md)]
 
 > [!CAUTION]
 > To use the recommended keyless authentication with the SDK, make sure that the `AZURE_OPENAI_API_KEY` environment variable isn't set. 
 
 ## Text in audio out
 
-#### [Microsoft Entra ID](#tab/javascript-keyless)
+#### [Microsoft Entra ID](#tab/keyless)
 
 1. Create the `text-in-audio-out.js` file with the following code:
 
@@ -143,7 +135,7 @@ For the recommended keyless authentication with Microsoft Entra ID, you need to:
     ```
 
 
-#### [API key](#tab/javascript-key)
+#### [API key](#tab/api-key)
 
 1. Create the `text-in-audio-out.js` file with the following code:
 

@@ -76,19 +76,11 @@ For the recommended keyless authentication with Microsoft Entra ID, you need to:
 
 ## Retrieve resource information
 
-#### [Microsoft Entra ID](#tab/javascript-keyless)
-
-[!INCLUDE [keyless-environment-variables](env-var-without-key.md)]
-
-#### [API key](#tab/javascript-key)
-
-[!INCLUDE [key-environment-variables](env-var-key.md)]
-
----
+[!INCLUDE [resource authentication](resource-auth.md)]
 
 ## Text in audio out
 
-## [Microsoft Entra ID](#tab/javascript-keyless)
+## [Microsoft Entra ID](#tab/keyless)
 
 1. Create the `text-in-audio-out.py` file with the following code:
 
@@ -149,7 +141,7 @@ For the recommended keyless authentication with Microsoft Entra ID, you need to:
     python text-in-audio-out.py
     ```
 
-## [API key](#tab/javascript-key)
+## [API key](#tab/api-key)
 
 1. Create the `text-in-audio-out.py` file with the following code:
 

@@ -15,7 +15,7 @@ ms.date: 12/26/2024
 - An Azure OpenAI resource created in the East US 2 or Sweden Central regions. See [Region availability](/azure/ai-services/openai/concepts/models#model-summary-table-and-region-availability).
 - Then, you need to deploy a `gpt-4o-realtime-preview` model with your Azure OpenAI resource. For more information, see [Create a resource and deploy a model with Azure OpenAI](../how-to/create-resource.md). 
 
-## Microsoft Entra ID prerequisites
+### Microsoft Entra ID prerequisites
 
 For the recommended keyless authentication with Microsoft Entra ID, you need to:
 - Install the [Azure CLI](/cli/azure/install-azure-cli) used for keyless authentication with Microsoft Entra ID.
@@ -60,22 +60,14 @@ For the recommended keyless authentication with Microsoft Entra ID, you need to:
 
 ## Retrieve resource information
 
-#### [Microsoft Entra ID](#tab/javascript-keyless)
-
-[!INCLUDE [keyless-environment-variables](env-var-without-key.md)]
-
-#### [API key](#tab/javascript-key)
-
-[!INCLUDE [key-environment-variables](env-var-key.md)]
-
----
+[!INCLUDE [resource authentication](resource-auth.md)]
 
 > [!CAUTION]
 > To use the recommended keyless authentication with the SDK, make sure that the `AZURE_OPENAI_API_KEY` environment variable isn't set. 
-    
+
 ## Text in audio out
 
-#### [Microsoft Entra ID](#tab/typescript-keyless)
+#### [Microsoft Entra ID](#tab/keyless)
 
 1. Create the `text-in-audio-out.ts` file with the following code:
 
@@ -170,7 +162,7 @@ For the recommended keyless authentication with Microsoft Entra ID, you need to:
     node text-in-audio-out.js
     ```
 
-#### [API key](#tab/typescript-key)
+#### [API key](#tab/api-key)
 
 1. Create the `text-in-audio-out.ts` file with the following code:
 

@@ -3,9 +3,23 @@ author: eric-urban
 ms.author: eur 
 ms.service: azure-ai-openai
 ms.topic: include
-ms.date: 12/27/2024
+ms.date: 1/7/2025
 ---
 
+You need to retrieve the following information to authenticate your application with your Azure OpenAI resource:
+
+#### [Microsoft Entra ID](#tab/keyless)
+
+|Variable name | Value |
+|--------------------------|-------------|
+| `AZURE_OPENAI_ENDPOINT`               | This value can be found in the **Keys and Endpoint** section when examining your resource from the Azure portal. |
+| `AZURE_OPENAI_DEPLOYMENT_NAME` | This value will correspond to the custom name you chose for your deployment when you deployed a model. This value can be found under **Resource Management** > **Model Deployments** in the Azure portal.|
+| `OPENAI_API_VERSION`|Learn more about [API Versions](/azure/ai-services/openai/api-version-deprecation).|
+
+Learn more about [keyless authentication](/azure/ai-services/authentication) and [setting environment variables](/azure/ai-services/cognitive-services-environment-variables).
+
+#### [API key](#tab/api-key)
+
 |Variable name | Value |
 |--------------------------|-------------|
 | `AZURE_OPENAI_ENDPOINT`               | This value can be found in the **Keys and Endpoint** section when examining your resource from the Azure portal. |
@@ -15,4 +29,9 @@ ms.date: 12/27/2024
 
 Learn more about [finding API keys](/azure/ai-services/cognitive-services-environment-variables) and [setting environment variables](/azure/ai-services/cognitive-services-environment-variables).
 
-[!INCLUDE [Azure key vault](~/reusable-content/ce-skilling/azure/includes/ai-services/security/azure-key-vault.md)]
\ No newline at end of file
+[!INCLUDE [Azure key vault](~/reusable-content/ce-skilling/azure/includes/ai-services/security/azure-key-vault.md)]
+
+---
+
+
+

@@ -16,7 +16,7 @@ GPT-4o, GPT-4o-mini, and GPT-4 are our most advanced models that can be fine-tun
 - Evaluation endpoints are in the same geography as the Azure OpenAI resource;
 - Training data is not stored in connection with performing evaluations; only the final model assessment (deployable or not deployable) is persisted; and
 
-GPT-4o, GPT-4o-mini, and GPT-4 fine-tuned model evaluation filters are set to predefined thresholds and cannot be modified by customers; they aren't tied to any custom content filtering configuration you may have created.
+GPT-4o, GPT-4o-mini, and GPT-4 fine-tuned model evaluation filters are set to predefined thresholds and cannot be modified by customers; they aren't tied to any custom content filtering configuration you might have created.
 
 ### Data evaluation
 

@@ -19,58 +19,18 @@ recommendations: false
 - [Azure CLI](/cli/azure/install-azure-cli) used for passwordless authentication in a local development environment, create the necessary context by signing in with the Azure CLI.
 - An Azure OpenAI resource created in a supported region (see [Region availability](/azure/ai-services/openai/concepts/models#model-summary-table-and-region-availability)). For more information, see [Create a resource and deploy a model with Azure OpenAI](../how-to/create-resource.md).
 
+### Microsoft Entra ID prerequisites
 
-## Set up
+For the recommended keyless authentication with Microsoft Entra ID, you need to:
+- Install the [Azure CLI](/cli/azure/install-azure-cli) used for keyless authentication with Microsoft Entra ID.
+- Assign the `Cognitive Services User` role to your user account. You can assign roles in the Azure portal under **Access control (IAM)** > **Add role assignment**.
 
-### Retrieve key and endpoint
+## Retrieve resource information
 
-To successfully make a call against Azure OpenAI, you need an **endpoint** and a **key**.
+[!INCLUDE [resource authentication](resource-auth.md)]
 
-|Variable name | Value |
-|--------------------------|-------------|
-| `AZURE_OPENAI_ENDPOINT`               | The service endpoint can be found in the **Keys & Endpoint** section when examining your resource from the Azure portal. Alternatively, you can find the endpoint via the **Deployments** page in Azure AI Foundry portal. An example endpoint is: `https://docs-test-001.openai.azure.com/`.|
-| `AZURE_OPENAI_API_KEY` | This value can be found in the **Keys & Endpoint** section when examining your resource from the Azure portal. You can use either `KEY1` or `KEY2`.|
-
-Go to your resource in the Azure portal. The **Endpoint and Keys** can be found in the **Resource Management** section. Copy your endpoint and access key as you need both for authenticating your API calls. You can use either `KEY1` or `KEY2`. Always having two keys allows you to securely rotate and regenerate keys without causing a service disruption.
-
-:::image type="content" source="../media/quickstarts/endpoint.png" alt-text="Screenshot of the overview UI for an Azure OpenAI resource in the Azure portal with the endpoint & access keys location highlighted." lightbox="../media/quickstarts/endpoint.png":::
-
-### Environment variables
-
-Create and assign persistent environment variables for your key and endpoint.
-
-[!INCLUDE [Azure key vault](~/reusable-content/ce-skilling/azure/includes/ai-services/security/azure-key-vault.md)]
-
-# [Command Line](#tab/command-line)
-
-```CMD
-setx AZURE_OPENAI_API_KEY "REPLACE_WITH_YOUR_KEY_VALUE_HERE" 
-```
-
-```CMD
-setx AZURE_OPENAI_ENDPOINT "REPLACE_WITH_YOUR_ENDPOINT_HERE" 
-```
-
-# [PowerShell](#tab/powershell)
-
-```powershell
-[System.Environment]::SetEnvironmentVariable('AZURE_OPENAI_API_KEY', 'REPLACE_WITH_YOUR_KEY_VALUE_HERE', 'User')
-```
-
-```powershell
-[System.Environment]::SetEnvironmentVariable('AZURE_OPENAI_ENDPOINT', 'REPLACE_WITH_YOUR_ENDPOINT_HERE', 'User')
-```
-
-# [Bash](#tab/bash)
-
-```Bash
-echo export AZURE_OPENAI_API_KEY="REPLACE_WITH_YOUR_KEY_VALUE_HERE" >> /etc/environment && source /etc/environment
-```
-
-```Bash
-echo export AZURE_OPENAI_ENDPOINT="REPLACE_WITH_YOUR_ENDPOINT_HERE" >> /etc/environment && source /etc/environment
-```
----
+> [!CAUTION]
+> To use the recommended keyless authentication with the SDK, make sure that the `AZURE_OPENAI_API_KEY` environment variable isn't set. 
 
 ## Create a Node application
 

@@ -20,58 +20,18 @@ recommendations: false
 - [Azure CLI](/cli/azure/install-azure-cli) used for passwordless authentication in a local development environment, create the necessary context by signing in with the Azure CLI.
 - An Azure OpenAI resource created in a supported region (see [Region availability](/azure/ai-services/openai/concepts/models#model-summary-table-and-region-availability)). For more information, see [Create a resource and deploy a model with Azure OpenAI](../how-to/create-resource.md).
 
+### Microsoft Entra ID prerequisites
 
-## Set up
+For the recommended keyless authentication with Microsoft Entra ID, you need to:
+- Install the [Azure CLI](/cli/azure/install-azure-cli) used for keyless authentication with Microsoft Entra ID.
+- Assign the `Cognitive Services User` role to your user account. You can assign roles in the Azure portal under **Access control (IAM)** > **Add role assignment**.
 
-### Retrieve key and endpoint
+## Retrieve resource information
 
-To successfully make a call against Azure OpenAI, you need an **endpoint** and a **key**.
+[!INCLUDE [resource authentication](resource-auth.md)]
 
-|Variable name | Value |
-|--------------------------|-------------|
-| `AZURE_OPENAI_ENDPOINT`               | This value can be found in the **Keys & Endpoint** section when examining your resource from the Azure portal. Alternatively, you can find the value in the **Azure OpenAI Studio** > **Playground** > **Code View**. An example endpoint is: `https://aoai-docs.openai.azure.com/`.|
-| `AZURE_OPENAI_API_KEY` | This value can be found in the **Keys & Endpoint** section when examining your resource from the Azure portal. You can use either `KEY1` or `KEY2`.|
-
-Go to your resource in the Azure portal. The **Endpoint and Keys** can be found in the **Resource Management** section. Copy your endpoint and access key as you need both for authenticating your API calls. You can use either `KEY1` or `KEY2`. Always having two keys allows you to securely rotate and regenerate keys without causing a service disruption.
-
-:::image type="content" source="../media/quickstarts/endpoint.png" alt-text="Screenshot of the overview UI for an Azure OpenAI resource in the Azure portal with the endpoint & access keys location highlighted." lightbox="../media/quickstarts/endpoint.png":::
-
-### Environment variables
-
-Create and assign persistent environment variables for your key and endpoint.
-
-[!INCLUDE [Azure key vault](~/reusable-content/ce-skilling/azure/includes/ai-services/security/azure-key-vault.md)]
-
-# [Command Line](#tab/command-line)
-
-```CMD
-setx AZURE_OPENAI_API_KEY "REPLACE_WITH_YOUR_KEY_VALUE_HERE" 
-```
-
-```CMD
-setx AZURE_OPENAI_ENDPOINT "REPLACE_WITH_YOUR_ENDPOINT_HERE" 
-```
-
-# [PowerShell](#tab/powershell)
-
-```powershell
-[System.Environment]::SetEnvironmentVariable('AZURE_OPENAI_API_KEY', 'REPLACE_WITH_YOUR_KEY_VALUE_HERE', 'User')
-```
-
-```powershell
-[System.Environment]::SetEnvironmentVariable('AZURE_OPENAI_ENDPOINT', 'REPLACE_WITH_YOUR_ENDPOINT_HERE', 'User')
-```
-
-# [Bash](#tab/bash)
-
-```Bash
-echo export AZURE_OPENAI_API_KEY="REPLACE_WITH_YOUR_KEY_VALUE_HERE" >> /etc/environment && source /etc/environment
-```
-
-```Bash
-echo export AZURE_OPENAI_ENDPOINT="REPLACE_WITH_YOUR_ENDPOINT_HERE" >> /etc/environment && source /etc/environment
-```
----
+> [!CAUTION]
+> To use the recommended keyless authentication with the SDK, make sure that the `AZURE_OPENAI_API_KEY` environment variable isn't set. 
 
 ## Create a Node application
 

@@ -14,7 +14,7 @@ ms.date: 10/22/2024
 [Source code](https://github.com/openai/openai-node) | [Package (npm)](https://www.npmjs.com/package/openai) | [Samples](https://github.com/Azure/azure-sdk-for-js/tree/main/sdk/openai/openai/samples)
 
 > [!NOTE]
-> This article has been updated to use the [latest OpenAI npm package](https://www.npmjs.com/package/openai) which now fully supports Azure OpenAI. If you are looking for code examples for the legacy Azure OpenAI JavaScript SDK they are currently still [available in this repo](https://github.com/Azure/azure-sdk-for-js/tree/main/sdk/openai/openai/samples/v2-beta/javascript).
+> This guide uses the [latest OpenAI npm package](https://www.npmjs.com/package/openai) which now fully supports Azure OpenAI. If you're looking for code examples for the legacy Azure OpenAI JavaScript SDK, they're currently still [available in this repo](https://github.com/Azure/azure-sdk-for-js/tree/main/sdk/openai/openai/samples/v2-beta/javascript).
 
 ## Prerequisites
 
@@ -24,23 +24,25 @@ ms.date: 10/22/2024
 - [Azure CLI](/cli/azure/install-azure-cli) used for passwordless authentication in a local development environment, create the necessary context by signing in with the Azure CLI.
 - An Azure OpenAI Service resource with the `gpt-35-turbo-instruct` model deployed. For more information about model deployment, see the [resource deployment guide](../how-to/create-resource.md).
 
-## Set up
 
-[!INCLUDE [get-key-endpoint](get-key-endpoint.md)]
+## Retrieve resource information
 
-[!INCLUDE [environment-variables](environment-variables.md)]
+[!INCLUDE [resource authentication](resource-auth.md)]
 
-In a console window (such as cmd, PowerShell, or Bash), create a new directory for your app, and navigate to it.
+> [!CAUTION]
+> To use the recommended keyless authentication with the SDK, make sure that the `AZURE_OPENAI_API_KEY` environment variable isn't set. 
 
 ## Install the client library
 
+In a console window (such as cmd, PowerShell, or Bash), create a new directory for your app, and navigate to it.
+
 Install the required packages for JavaScript with npm from within the context of your new directory:
 
 ```console
 npm install openai @azure/identity
 ```
 
-Your app's _package.json_ file will be updated with the dependencies.
+Your app's _package.json_ file is updated with the dependencies.
 
 
 ## Create a sample application
@@ -200,7 +202,7 @@ Microsoft was founded on April 4, 1975.
 ```
 
 > [!NOTE]
-> If your receive the error: *Error occurred: OpenAIError: The `apiKey` and `azureADTokenProvider` arguments are mutually exclusive; only one can be passed at a time.* You may need to remove a pre-existing environment variable for the API key from your system. Even though the Microsoft Entra ID code sample is not explicitly referencing the API key environment variable, if one is present on the system executing this sample, this error will still be generated.
+> If your receive the error: *Error occurred: OpenAIError: The `apiKey` and `azureADTokenProvider` arguments are mutually exclusive; only one can be passed at a time.* You might need to remove a preexisting environment variable for the API key from your system. Even though the Microsoft Entra ID code sample isn't explicitly referencing the API key environment variable, if one is present on the system executing this sample, this error is still generated.
 
 
 ## Clean up resources

@@ -1,100 +1,41 @@
 ---
-#services: cognitive-services
-manager: nitinme
-author: travisw
-ms.author: travisw
+author: eric-urban 
+ms.author: eur 
 ms.service: azure-ai-openai
 ms.topic: include
-ms.date: 08/29/2023
+ms.date: 1/7/2025
 ---
 
-## Retrieve required variables
+## Retrieve resource information
 
-To successfully make a call against Azure OpenAI, you need the following variables. This quickstart assumes you've uploaded your data to an Azure blob storage account and have an Azure AI Search index created. See [Add your data using Azure AI Foundry](../use-your-data-quickstart.md?pivots=programming-language-studio)
+You need to retrieve the following information to authenticate your application with your Azure OpenAI resource. This quickstart assumes you've uploaded your data to an Azure blob storage account and have an Azure AI Search index created. See [Add your data using Azure AI Foundry portal](../use-your-data-quickstart.md?pivots=programming-language-studio).
+
+#### [Microsoft Entra ID](#tab/keyless)
 
 |Variable name | Value |
 |--------------------------|-------------|
-| `AZURE_OPENAI_ENDPOINT`               | This value can be found in the **Keys & Endpoint** section when examining your Azure OpenAI resource from the Azure portal. Alternatively, you can find the value in **Azure AI Foundry** > **Chat playground** > **Code view**. An example endpoint is: `https://my-resoruce.openai.azure.com`.|
-| `AZURE_OPENAI_API_KEY` | This value can be found in **Resource management** > **Keys & Endpoint** section when examining your Azure OpenAI resource from the Azure portal. You can use either `KEY1` or `KEY2`. Always having two keys allows you to securely rotate and regenerate keys without causing a service disruption. |
-| `AZURE_OPENAI_DEPLOYMENT_ID` | This value corresponds to the custom name you chose for your deployment when you deployed a model. This value can be found under **Resource Management** > **Deployments** in the Azure portal or alternatively under **Management** > **Deployments** in Azure AI Foundry portal.|
+| `AZURE_OPENAI_ENDPOINT`               | This value can be found in the **Keys & Endpoint** section when examining your Azure OpenAI resource from the Azure portal. An example endpoint is: `https://my-resoruce.openai.azure.com`.|
+| `AZURE_OPENAI_DEPLOYMENT_ID` | This value corresponds to the custom name you chose for your deployment when you deployed a model. This value can be found under **Resource Management** > **Deployments** in the Azure portal.|
 | `AZURE_AI_SEARCH_ENDPOINT` | This value can be found in the **Overview** section when examining your Azure AI Search resource from the Azure portal. |
-| `AZURE_AI_SEARCH_API_KEY` | This value can be found in the **Settings** > **Keys** section when examining your Azure AI Search resource from the Azure portal. You can use either the primary admin key or secondary admin key. Always having two keys allows you to securely rotate and regenerate keys without causing a service disruption. |
 | `AZURE_AI_SEARCH_INDEX` | This value corresponds to the name of the index you created to store your data. You can find it in the **Overview** section when examining your Azure AI Search resource from the Azure portal. |
 
-### Environment variables
-
-Create and assign persistent environment variables for your key and endpoint.
-
-[!INCLUDE [Azure key vault](~/reusable-content/ce-skilling/azure/includes/ai-services/security/azure-key-vault.md)]
-
-# [Command Line](#tab/command-line)
-
-```CMD
-setx AZURE_OPENAI_ENDPOINT REPLACE_WITH_YOUR_AOAI_ENDPOINT_VALUE_HERE
-```
-```CMD
-setx AZURE_OPENAI_API_KEY REPLACE_WITH_YOUR_AOAI_KEY_VALUE_HERE
-```
-```CMD
-setx AZURE_OPENAI_DEPLOYMENT_ID REPLACE_WITH_YOUR_AOAI_DEPLOYMENT_VALUE_HERE
-```
-```CMD
-setx AZURE_AI_SEARCH_ENDPOINT REPLACE_WITH_YOUR_AZURE_SEARCH_RESOURCE_VALUE_HERE
-```
-```CMD
-setx AZURE_AI_SEARCH_API_KEY REPLACE_WITH_YOUR_AZURE_SEARCH_RESOURCE_KEY_VALUE_HERE
-```
-```CMD
-setx AZURE_AI_SEARCH_INDEX REPLACE_WITH_YOUR_INDEX_NAME_HERE
-```
-
-
-# [PowerShell](#tab/powershell)
-
-```powershell
-[System.Environment]::SetEnvironmentVariable('AZURE_OPENAI_ENDPOINT', 'REPLACE_WITH_YOUR_AOAI_ENDPOINT_VALUE_HERE', 'User')
-```
+Learn more about [keyless authentication](/azure/ai-services/authentication) and [setting environment variables](/azure/ai-services/cognitive-services-environment-variables).
 
-```powershell
-[System.Environment]::SetEnvironmentVariable('AZURE_OPENAI_API_KEY', 'REPLACE_WITH_YOUR_AOAI_KEY_VALUE_HERE', 'User')
-```
+#### [API key](#tab/api-key)
 
-```powershell
-[System.Environment]::SetEnvironmentVariable('AZURE_OPENAI_DEPLOYMENT_ID', 'REPLACE_WITH_YOUR_AOAI_DEPLOYMENT_VALUE_HERE', 'User')
-```
-
-```powershell
-[System.Environment]::SetEnvironmentVariable('AZURE_AI_SEARCH_ENDPOINT', 'REPLACE_WITH_YOUR_AZURE_SEARCH_RESOURCE_VALUE_HERE', 'User')
-```
-
-```powershell
-[System.Environment]::SetEnvironmentVariable('AZURE_AI_SEARCH_API_KEY', 'REPLACE_WITH_YOUR_AZURE_SEARCH_RESOURCE_KEY_VALUE_HERE', 'User')
-```
-
-```powershell
-[System.Environment]::SetEnvironmentVariable('AZURE_AI_SEARCH_INDEX', 'REPLACE_WITH_YOUR_INDEX_NAME_HERE', 'User')
-```
+|Variable name | Value |
+|--------------------------|-------------|
+| `AZURE_OPENAI_ENDPOINT`               | This value can be found in the **Keys & Endpoint** section when examining your Azure OpenAI resource from the Azure portal. An example endpoint is: `https://my-resoruce.openai.azure.com`.|
+| `AZURE_OPENAI_API_KEY` | This value can be found in **Resource management** > **Keys & Endpoint** section when examining your Azure OpenAI resource from the Azure portal. You can use either `KEY1` or `KEY2`. Always having two keys allows you to securely rotate and regenerate keys without causing a service disruption. |
+| `AZURE_OPENAI_DEPLOYMENT_ID` | This value corresponds to the custom name you chose for your deployment when you deployed a model. This value can be found under **Resource Management** > **Deployments** in the Azure portal.|
+| `AZURE_AI_SEARCH_ENDPOINT` | This value can be found in the **Overview** section when examining your Azure AI Search resource from the Azure portal. |
+| `AZURE_AI_SEARCH_API_KEY` | This value can be found in the **Settings** > **Keys** section when examining your Azure AI Search resource from the Azure portal. You can use either the primary admin key or secondary admin key. Always having two keys allows you to securely rotate and regenerate keys without causing a service disruption. |
+| `AZURE_AI_SEARCH_INDEX` | This value corresponds to the name of the index you created to store your data. You can find it in the **Overview** section when examining your Azure AI Search resource from the Azure portal. |
 
-# [Bash](#tab/bash)
+Learn more about [finding API keys](/azure/ai-services/cognitive-services-environment-variables) and [setting environment variables](/azure/ai-services/cognitive-services-environment-variables).
 
-```Bash
-export AZURE_OPENAI_ENDPOINT=REPLACE_WITH_YOUR_AOAI_ENDPOINT_VALUE_HERE
-```
-```Bash
-export AZURE_OPENAI_API_KEY=REPLACE_WITH_YOUR_AOAI_KEY_VALUE_HERE
-```
-```Bash
-export AZURE_OPENAI_DEPLOYMENT_ID=REPLACE_WITH_YOUR_AOAI_DEPLOYMENT_VALUE_HERE
-```
-```Bash
-export AZURE_AI_SEARCH_ENDPOINT=REPLACE_WITH_YOUR_AZURE_SEARCH_RESOURCE_VALUE_HERE
-```
-```Bash
-export AZURE_AI_SEARCH_API_KEY=REPLACE_WITH_YOUR_AZURE_SEARCH_RESOURCE_KEY_VALUE_HERE
-```
-```Bash
-export AZURE_AI_SEARCH_INDEX=REPLACE_WITH_YOUR_INDEX_NAME_HERE
-```
+[!INCLUDE [Azure key vault](~/reusable-content/ce-skilling/azure/includes/ai-services/security/azure-key-vault.md)]
 
 ---
 
+

@@ -11,7 +11,6 @@ ms.date: 10/22/2024
 [!INCLUDE [Set up required variables](./use-your-data-common-variables.md)]
 
 
-
 ## Initialize a Node.js application
 
 In a console window (such as cmd, PowerShell, or Bash), create a new directory for your app, and navigate to it. Then run the `npm init` command to create a node application with a _package.json_ file.

@@ -78,4 +78,4 @@ The available health plans in the Contoso Electronics plan and benefit packages
 
 ## Chat with your model using a web app
 
-To start chatting with the Azure OpenAI model that uses your data, you can deploy a web app using [Azure OpenAI studio](../concepts/use-your-data.md#deploy-to-a-copilot-preview-teams-app-preview-or-web-app) or example code we [provide on GitHub](https://go.microsoft.com/fwlink/?linkid=2244395). This app deploys using Azure app service, and provides a user interface for sending queries. This app can be used with Azure OpenAI models that use your data, or models that don't use your data. See the readme file in the repo for instructions on requirements, setup, and deployment. You can optionally customize the [frontend and backend logic](../how-to/use-web-app.md#customizing-the-application-using-environment-variables) of the web app by making changes to the source code.
+To start chatting with the Azure OpenAI model that uses your data, you can deploy a web app using [Azure AI Foundry portal](../concepts/use-your-data.md#deploy-to-a-copilot-preview-teams-app-preview-or-web-app) or example code we [provide on GitHub](https://go.microsoft.com/fwlink/?linkid=2244395). This app deploys using Azure app service, and provides a user interface for sending queries. This app can be used with Azure OpenAI models that use your data, or models that don't use your data. See the readme file in the repo for instructions on requirements, setup, and deployment. You can optionally customize the [frontend and backend logic](../how-to/use-web-app.md#customizing-the-application-using-environment-variables) of the web app by making changes to the source code.

@@ -87,4 +87,4 @@ curl -i -X POST $AZURE_OPENAI_ENDPOINT/openai/deployments/$AZURE_OPENAI_DEPLOYME
 
 ## Chat with your model using a web app
 
-To start chatting with the Azure OpenAI model that uses your data, you can deploy a web app using [Azure OpenAI studio](../concepts/use-your-data.md#deploy-to-a-copilot-preview-teams-app-preview-or-web-app) or example code we [provide on GitHub](https://go.microsoft.com/fwlink/?linkid=2244395). This app deploys using Azure app service, and provides a user interface for sending queries. This app can be used with Azure OpenAI models that use your data, or models that don't use your data. See the readme file in the repo for instructions on requirements, setup, and deployment. You can optionally customize the [frontend and backend logic](../how-to/use-web-app.md#customizing-the-application-using-environment-variables) of the web app by making changes to the source code.
+To start chatting with the Azure OpenAI model that uses your data, you can deploy a web app using [Azure AI Foundry portal](../concepts/use-your-data.md#deploy-to-a-copilot-preview-teams-app-preview-or-web-app) or example code we [provide on GitHub](https://go.microsoft.com/fwlink/?linkid=2244395). This app deploys using Azure app service, and provides a user interface for sending queries. This app can be used with Azure OpenAI models that use your data, or models that don't use your data. See the readme file in the repo for instructions on requirements, setup, and deployment. You can optionally customize the [frontend and backend logic](../how-to/use-web-app.md#customizing-the-application-using-environment-variables) of the web app by making changes to the source code.

@@ -18,7 +18,7 @@ recommendations: false
 
 Start exploring Azure OpenAI capabilities with a no-code approach through the chat playground. It's simply a text box where you can submit a prompt to generate a completion. From this page, you can quickly iterate and experiment with the capabilities. 
 
-:::image type="content" source="../media/quickstarts/chat-playground-new.png" alt-text="Screenshot of the playground page of the Azure OpenAI Studio with sections highlighted." lightbox="../media/quickstarts/chat-playground-new.png":::
+:::image type="content" source="../media/quickstarts/chat-playground-new.png" alt-text="Screenshot of the playground page of the Azure AI Foundry portal with sections highlighted." lightbox="../media/quickstarts/chat-playground-new.png":::
 
 The playground gives you options to tailor your chat experience. On the top menu, you can select **Deploy** to determine which model generates a response using the search results from your index. You choose the number of past messages to include as conversation history for future generated responses. [Conversation history](../concepts/use-your-data.md#conversation-history-for-better-results) gives context to generate related responses but also consumes [token usage](../concepts/use-your-data.md#token-usage-estimation-for-azure-openai-on-your-data). The input token progress indicator keeps track of the token count of the question you submit. 
 
@@ -38,6 +38,6 @@ Send your first query. The chat models perform best in question and answer exerc
 
 Queries that require data analysis would probably fail, such as "*Which health plan is most popular?*". Queries that require information about all of your data will also likely fail, such as "*How many documents have I uploaded?*". Remember that the search engine looks for chunks having exact or similar terms, phrases, or construction to the query. And while the model might understand the question, if search results are chunks from the data set, it's not the right information to answer that kind of question.
 
-Chats are constrained by the number of documents (chunks) returned in the response (limited to 3-20 in Azure OpenAI Studio playground). As you can imagine, posing a question about "all of the titles" requires a full scan of the entire vector store.
+Chats are constrained by the number of documents (chunks) returned in the response (limited to 3-20 in Azure AI Foundry portal playground). As you can imagine, posing a question about "all of the titles" requires a full scan of the entire vector store.
 
 [!INCLUDE [deploy-web-app](deploy-web-app.md)]

@@ -13,73 +13,23 @@ author: eric-urban
 
 ## Prerequisites
 
-
 - An Azure subscription - [Create one for free](https://azure.microsoft.com/free/cognitive-services?azure-portal=true)
 - [LTS versions of Node.js](https://github.com/nodejs/release#release-schedule)
 - [Azure CLI](/cli/azure/install-azure-cli) used for passwordless authentication in a local development environment, create the necessary context by signing in with the Azure CLI.
 - An Azure OpenAI resource created in a supported region (see [Region availability](/azure/ai-services/openai/concepts/models#model-summary-table-and-region-availability)). For more information, see [Create a resource and deploy a model with Azure OpenAI](../how-to/create-resource.md).
 
+### Microsoft Entra ID prerequisites
 
+For the recommended keyless authentication with Microsoft Entra ID, you need to:
+- Install the [Azure CLI](/cli/azure/install-azure-cli) used for keyless authentication with Microsoft Entra ID.
+- Assign the `Cognitive Services User` role to your user account. You can assign roles in the Azure portal under **Access control (IAM)** > **Add role assignment**.
 
-## Set up
-
-### Retrieve key and endpoint
-
-To successfully make a call against Azure OpenAI, you need an *endpoint* and a *key*.
-
-|Variable name | Value |
-|--------------------------|-------------|
-| `AZURE_OPENAI_ENDPOINT`               | The service endpoint can be found in the **Keys & Endpoint** section when examining your resource from the Azure portal. Alternatively, you can find the endpoint via the **Deployments** page in Azure AI Foundry portal. An example endpoint is: `https://docs-test-001.openai.azure.com/`.|
-| `AZURE_OPENAI_API_KEY` | This value can be found in the **Keys & Endpoint** section when examining your resource from the Azure portal. You can use either `KEY1` or `KEY2`.|
-
-Go to your resource in the Azure portal. The **Endpoint and Keys** can be found in the **Resource Management** section. Copy your endpoint and access key as you'll need both for authenticating your API calls. You can use either `KEY1` or `KEY2`. Always having two keys allows you to securely rotate and regenerate keys without causing a service disruption.
-
-:::image type="content" source="../media/quickstarts/endpoint.png" alt-text="Screenshot of the overview UI for an Azure OpenAI resource in the Azure portal with the endpoint & access keys location circled in red." lightbox="../media/quickstarts/endpoint.png":::
-
-### Environment variables
-
-Create and assign persistent environment variables for your key and endpoint.
-
-[!INCLUDE [Azure key vault](~/reusable-content/ce-skilling/azure/includes/ai-services/security/azure-key-vault.md)]
-
-# [Command Line](#tab/command-line)
-
-```CMD
-setx AZURE_OPENAI_API_KEY "REPLACE_WITH_YOUR_KEY_VALUE_HERE" 
-```
-
-```CMD
-setx AZURE_OPENAI_ENDPOINT "REPLACE_WITH_YOUR_ENDPOINT_HERE" 
-```
-
-# [PowerShell](#tab/powershell)
-
-```powershell
-[System.Environment]::SetEnvironmentVariable('AZURE_OPENAI_API_KEY', 'REPLACE_WITH_YOUR_KEY_VALUE_HERE', 'User')
-```
-
-```powershell
-[System.Environment]::SetEnvironmentVariable('AZURE_OPENAI_ENDPOINT', 'REPLACE_WITH_YOUR_ENDPOINT_HERE', 'User')
-```
-
-# [Bash](#tab/bash)
-
-```Bash
-echo export AZURE_OPENAI_API_KEY="REPLACE_WITH_YOUR_KEY_VALUE_HERE" >> /etc/environment && source /etc/environment
-```
-
-```Bash
-echo export AZURE_OPENAI_ENDPOINT="REPLACE_WITH_YOUR_ENDPOINT_HERE" >> /etc/environment && source /etc/environment
-```
----
-
-## Microsoft Entra ID authentication is recommended
+## Retrieve resource information
 
-For passwordless authentication, you need to 
+[!INCLUDE [resource authentication](resource-auth.md)]
 
-1. Use the `@azure/identity` package.
-1. Assign the `Cognitive Services User` role to your user account. This can be done in the Azure portal under **Access control (IAM)** > **Add role assignment**.
-1. Sign in with the Azure CLI such as `az login`.
+> [!CAUTION]
+> To use the recommended keyless authentication with the SDK, make sure that the `AZURE_OPENAI_API_KEY` environment variable isn't set. 
 
 ## Create a Node application