@@ -33,7 +33,7 @@ Assistants API supports persistent automatically managed threads. This means tha
 > [!TIP]
 > There is no additional [pricing](https://azure.microsoft.com/pricing/details/cognitive-services/openai-service/) or [quota](../quotas-limits.md) for using Assistants unless you use the [code interpreter](../how-to/code-interpreter.md) or [file search](../how-to/file-search.md) tools.
 
-Assistants API is built on the same capabilities that power OpenAI’s GPT product. Some possible use cases range from AI-powered product recommender, sales analyst app, coding assistant, employee Q&A chatbot, and more. Start building on the no-code Assistants playground on the Azure OpenAI Studio, AI Studio, or start building with the API.
+Assistants API is built on the same capabilities that power OpenAI’s GPT product. Some possible use cases range from AI-powered product recommender, sales analyst app, coding assistant, employee Q&A chatbot, and more. Start building on the no-code Assistants playground on the Azure AI Studio or start building with the API.
 
 > [!IMPORTANT]
 > Retrieving untrusted data using Function calling, Code Interpreter or File Search with file input, and Assistant Threads functionalities could compromise the security of your Assistant, or the application that uses the Assistant. Learn about mitigation approaches [here](https://aka.ms/oai/assistant-rai).

@@ -842,15 +842,15 @@ Customers must understand that while the feature improves latency, it's a trade-
 
 **Customer Copyright Commitment**: Content that is retroactively flagged as protected material may not be eligible for Customer Copyright Commitment coverage. 
 
-To enable Asynchronous Filter in Azure OpenAI Studio, follow the [Content filter how-to guide](/azure/ai-services/openai/how-to/content-filters) to create a new content filtering configuration, and select **Asynchronous Filter** in the Streaming section.
+To enable Asynchronous Filter in Azure AI Studio, follow the [Content filter how-to guide](/azure/ai-services/openai/how-to/content-filters) to create a new content filtering configuration, and select **Asynchronous Filter** in the Streaming section.
 
 ### Comparison of content filtering modes
 
 | Compare | Streaming - Default | Streaming - Asynchronous Filter |
 |---|---|---|
 |Status |GA |Public Preview |
 | Eligibility |All customers |Customers approved for modified content filtering |
-| How to enable | Enabled by default, no action needed |Customers approved for modified content filtering can configure it directly in Azure OpenAI Studio (as part of a content filtering configuration, applied at the deployment level) |
+| How to enable | Enabled by default, no action needed |Customers approved for modified content filtering can configure it directly in Azure AI Studio (as part of a content filtering configuration, applied at the deployment level) |
 |Modality and availability |Text; all GPT models |Text; all GPT models |
 |Streaming experience |Content is buffered and returned in chunks |Zero latency (no buffering, filters run asynchronously) |
 |Content filtering signal |Immediate filtering signal |Delayed filtering signal (in up to ~1,000-character increments) |

@@ -77,13 +77,13 @@ This section describes the limitations of GPT-4 Turbo with Vision.
 
 - **Maximum input image size**: The maximum size for input images is restricted to 20 MB.
 - **Low resolution accuracy**: When images are analyzed using the "low resolution" setting, it allows for faster responses and uses fewer input tokens for certain use cases. However, this could impact the accuracy of object and text recognition within the image.
-- **Image chat restriction**: When you upload images in Azure OpenAI Studio or the API, there is a limit of 10 images per chat call.
+- **Image chat restriction**: When you upload images in Azure AI Studio or the API, there is a limit of 10 images per chat call.
 
 ### Video support
 
 - **Low resolution**: Video frames are analyzed using GPT-4 Turbo with Vision's "low resolution" setting, which may affect the accuracy of small object and text recognition in the video.
-- **Video file limits**: Both MP4 and MOV file types are supported. In Azure OpenAI Studio, videos must be less than 3 minutes long. When you use the API there is no such limitation.
-- **Prompt limits**: Video prompts only contain one video and no images. In Azure OpenAI Studio, you can clear the session to try another video or images.
+- **Video file limits**: Both MP4 and MOV file types are supported. In Azure AI Studio, videos must be less than 3 minutes long. When you use the API there is no such limitation.
+- **Prompt limits**: Video prompts only contain one video and no images. In Azure AI Studio, you can clear the session to try another video or images.
 - **Limited frame selection**: The service selects 20 frames from the entire video, which might not capture all the critical moments or details. Frame selection can be approximately evenly spread through the video or focused by a specific video retrieval query, depending on the prompt.
 - **Language support**: The service primarily supports English for grounding with transcripts. Transcripts don't provide accurate information on lyrics in songs.
 

@@ -511,7 +511,7 @@ These models can only be used with Embedding API requests.
 
 ## Assistants (Preview)
 
-For Assistants you need a combination of a supported model, and a supported region. Certain tools and capabilities require the latest models. The following models are available in the Assistants API, SDK, Azure AI Studio and Azure OpenAI Studio. The following table is for pay-as-you-go. For information on Provisioned Throughput Unit (PTU) availability, see [provisioned throughput](./provisioned-throughput.md). The listed models and regions can be used with both Assistants v1 and v2. You can use [global standard models](#global-standard-model-availability) if they are supported in the regions listed below. 
+For Assistants you need a combination of a supported model, and a supported region. Certain tools and capabilities require the latest models. The following models are available in the Assistants API, SDK, and Azure AI Studio. The following table is for pay-as-you-go. For information on Provisioned Throughput Unit (PTU) availability, see [provisioned throughput](./provisioned-throughput.md). The listed models and regions can be used with both Assistants v1 and v2. You can use [global standard models](#global-standard-model-availability) if they are supported in the regions listed below. 
 
 | Region | `gpt-35-turbo (0613)` | `gpt-35-turbo (1106)`| `fine tuned gpt-3.5-turbo-0125` | `gpt-4 (0613)` | `gpt-4 (1106)` | `gpt-4 (0125)` | `gpt-4o (2024-05-13)` | `gpt-4o-mini (2024-07-18)` |
 |-----|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|

@@ -65,29 +65,29 @@ sections:
         answer: | 
           Check out our [introduction to prompt engineering](./concepts/prompt-engineering.md). While these models are powerful, their behavior is also very sensitive to the prompts they receive from the user. This makes prompt construction an important skill to develop. After you've completed the introduction, check out our article on [system messages](./concepts/advanced-prompt-engineering.md).
       - question: |
-          My guest account has been given access to an Azure OpenAI resource, but I'm unable to access that resource in the Azure OpenAI Studio. How do I enable access?
+          My guest account has been given access to an Azure OpenAI resource, but I'm unable to access that resource in the Azure AI Studio. How do I enable access?
         answer: | 
-          This is expected behavior when using the default sign-in experience for the [Azure OpenAI Studio](https://oai.azure.com).
+          This is expected behavior when using the default sign-in experience for the [Azure AI Studio](https://ai.azure.com).
           
-          To access Azure OpenAI Studio from a guest account that has been granted access to an Azure OpenAI resource:
+          To access Azure AI Studio from a guest account that has been granted access to an Azure OpenAI resource:
           
-          1. Open a private browser session and then navigate to [https://oai.azure.com](https://oai.azure.com).
+          1. Open a private browser session and then navigate to [https://ai.azure.com](https://ai.azure.com).
           2. Rather than immediately entering your guest account credentials instead select `Sign-in options` 
           3. Now select **Sign in to an organization** 
           4. Enter the domain name of the organization that granted your guest account access to the Azure OpenAI resource. 
           5. Now sign-in with your guest account credentials. 
           
-          You should now be able to access the resource via the Azure OpenAI Studio.
+          You should now be able to access the resource via the Azure AI Studio.
           
-          Alternatively if you're signed into the [Azure portal](https://portal.azure.com) from the Azure OpenAI resource's Overview pane you can select **Go to Azure OpenAI Studio** to automatically sign in with the appropriate organizational context.   
+          Alternatively if you're signed into the [Azure portal](https://portal.azure.com) from the Azure OpenAI resource's Overview pane you can select **Go to Azure AI Studio** to automatically sign in with the appropriate organizational context.   
       - question: |
           When I ask GPT-4 which model it's running, it tells me it's running GPT-3. Why does this happen?
         answer: | 
           Azure OpenAI models (including GPT-4) being unable to correctly identify what model is running is expected behavior. 
 
           **Why does this happen?**
 
-          Ultimately, the model is performing next [token](/semantic-kernel/prompt-engineering/tokens) prediction in response to your question. The model doesn't have any native ability to query what model version is currently being run to answer your question. To answer this question, you can always go to **Azure OpenAI Studio** > **Management** > **Deployments** > and consult the model name column to confirm what model is currently associated with a given deployment name.
+          Ultimately, the model is performing next [token](/semantic-kernel/prompt-engineering/tokens) prediction in response to your question. The model doesn't have any native ability to query what model version is currently being run to answer your question. To answer this question, you can always go to **Azure AI Studio** > **Management** > **Deployments** > and consult the model name column to confirm what model is currently associated with a given deployment name.
 
           The questions, "What model are you running?" or "What is the latest model from OpenAI?" produce similar quality results to asking the model what the weather will be today. It might return the correct result, but purely by chance. On its own, the model has no real-world information other than what was part of its training/training data. In the case of GPT-4, as of August 2023 the underlying training data goes only up to September 2021. GPT-4 wasn't released until March 2023, so barring OpenAI releasing a new version with updated training data, or a new version that is fine-tuned to answer those specific questions, it's expected behavior for GPT-4 to respond that GPT-3 is the latest model release from OpenAI.
 
@@ -163,7 +163,7 @@ sections:
         answer: |
           Consult the Azure OpenAI [model availability guide](./concepts/models.md#model-summary-table-and-region-availability) for region availability.
       - question: |
-          How do I enable fine-tuning? Create a custom model is greyed out in Azure OpenAI Studio.  
+          How do I enable fine-tuning? Create a custom model is greyed out in Azure AI Studio.  
         answer: |
           In order to successfully access fine-tuning, you need Cognitive Services OpenAI Contributor assigned. Even someone with high-level Service Administrator permissions would still need this account explicitly set in order to access fine-tuning. For more information, please review the [role-based access control guidance](/azure/ai-services/openai/how-to/role-based-access-control#cognitive-services-openai-contributor).
       - question: |
@@ -298,7 +298,7 @@ sections:
       - question: |
           Will my web app be overwritten when I deploy the app again from the Azure AI Studio?
         answer:
-          Your app code won't be overwritten when you update your app. The app will be updated to use the Azure OpenAI resource, Azure AI Search index (if you're using Azure OpenAI on your data), and model settings selected in the Azure OpenAI Studio without any change to the appearance or functionality. 
+          Your app code won't be overwritten when you update your app. The app will be updated to use the Azure OpenAI resource, Azure AI Search index (if you're using Azure OpenAI on your data), and model settings selected in the Azure AI Studio without any change to the appearance or functionality. 
   - name: Using your data
     questions:
       - question: |
@@ -346,7 +346,7 @@ sections:
         answer:
           You must send queries in the same language of your data. Your data can be in any of the languages supported by [Azure AI Search](/azure/search/search-language-support).
       - question: |
-          If Semantic Search is enabled for my Azure AI Search resource, will it be automatically applied to Azure OpenAI on your data in the Azure OpenAI Studio?
+          If Semantic Search is enabled for my Azure AI Search resource, will it be automatically applied to Azure OpenAI on your data in the Azure AI Studio?
         answer:
           When you select "Azure AI Search" as the data source, you can choose to apply semantic search. 
           If you select "Azure Blob Container" or "Upload files" as the data source, you can create the index as usual. Afterwards you would reingest the data using the "Azure AI Search" option to select the same index and apply Semantic Search. You will then be ready to chat on your data with semantic search applied.

@@ -86,7 +86,7 @@ The following aren't currently supported:
 
 In the Studio UI the deployment type will appear as `Global-Batch`.
 
-:::image type="content" source="../media/how-to/global-batch/global-batch.png" alt-text="Screenshot that shows the model deployment dialog in Azure OpenAI Studio with Global-Batch deployment type highlighted." lightbox="../media/how-to/global-batch/global-batch.png":::
+:::image type="content" source="../media/how-to/global-batch/global-batch.png" alt-text="Screenshot that shows the model deployment dialog in Azure AI Studio with Global-Batch deployment type highlighted." lightbox="../media/how-to/global-batch/global-batch.png":::
 
 > [!TIP]
 > We recommend enabling **dynamic quota** for all global batch model deployments to help avoid job failures due to insufficient enqueued token quota. Dynamic quota allows your deployment to opportunistically take advantage of more quota when extra capacity is available. When dynamic quota is set to off, your deployment will only be able to process requests up to the enqueued token limit that was defined when you created the deployment.

@@ -20,7 +20,7 @@ Azure OpenAI Service provides a **completion endpoint** that can be used for a w
 > [!IMPORTANT]
 > Unless you have a specific use case that requires the completions endpoint, we recommend instead using the [chat completions endpoint](./chatgpt.md) which allows you to take advantage of the latest models like GPT-4o, GPT-4o mini, and GPT-4 Turbo. 
 
-The best way to start exploring completions is through the playground in [Azure OpenAI Studio](https://oai.azure.com). It's a simple text box where you enter a prompt to generate a completion. You can start with a simple prompt like this one:
+The best way to start exploring completions is through the playground in [Azure AI Studio](https://ai.azure.com). It's a simple text box where you enter a prompt to generate a completion. You can start with a simple prompt like this one:
 
 ```console
 write a tagline for an ice cream shop

@@ -119,7 +119,7 @@ You can use the following policy to disable access to Azure OpenAI global standa
 
 ## Deploy models
 
-:::image type="content" source="../media/deployment-types/deploy-models-new.png" alt-text="Screenshot that shows the model deployment dialog in Azure OpenAI Studio with three deployment types highlighted." lightbox="../media/deployment-types/deploy-models-new.png":::
+:::image type="content" source="../media/deployment-types/deploy-models-new.png" alt-text="Screenshot that shows the model deployment dialog in Azure AI Studio with three deployment types highlighted." lightbox="../media/deployment-types/deploy-models-new.png":::
 
 To learn about creating resources and deploying models refer to the [resource creation guide](./create-resource.md).
 

@@ -44,11 +44,11 @@ The flexibility to distribute TPM globally within a subscription and region has
 
 When you create a model deployment, you have the option to assign Tokens-Per-Minute (TPM) to that deployment. TPM can be modified in increments of 1,000, and will map to the TPM and RPM rate limits enforced on your deployment, as discussed above.
 
-To create a new deployment from within the Azure AI Studio under **Shared Resources** select **Deployments** > **Deploy model** > **Deploy base model** > **Select Model** > **Confirm**.
+To create a new deployment from within the Azure AI Studio select **Deployments** > **Deploy model** > **Deploy base model** > **Select Model** > **Confirm**.
 
 :::image type="content" source="../media/quota/deployment-new.png" alt-text="Screenshot of the deployment UI of Azure AI Studio" lightbox="../media/quota/deployment-new.png":::
 
-Post deployment you can adjust your TPM allocation by selecting **Edit** under **Shared resources** > **Deployments** in Azure OpenAI Studio. You can also modify this selection within the new quota management experience under **Management** > **Quotas**.
+Post deployment you can adjust your TPM allocation by selecting and editing your model from the **Deployments** page in Azure AI Studio. You can also modify this setting from the **Management** > **Model quota** page.
 
 > [!IMPORTANT]
 > Quotas and limits are subject to change, for the most up-date-information consult our [quotas and limits article](../quotas-limits.md).

@@ -28,7 +28,7 @@ You can use Codex for a variety of tasks including:
 
 ## How to use completions models with code
 
-Here are a few examples of using Codex that can be tested in [Azure OpenAI Studio's](https://oai.azure.com) playground with a deployment of a Codex series model, such as `code-davinci-002`.
+Here are a few examples of using Codex that can be tested in the [Azure AI Studio](https://ai.azure.com) playground with a deployment of a Codex series model, such as `code-davinci-002`.
 
 ### Saying "Hello" (Python)
 

@@ -22,8 +22,7 @@ author: mrbullwinkle
 
 ## Go to the Azure AI Studio (Preview)
 
-> [!NOTE]
-> While you can use [Azure OpenAI Studio](https://oai.azure.com/), we recommend using the steps here to use [Azure AI Studio](https://ai.azure.com). AI Studio lets you use Assistants v2 which provides several upgrades such as the [file search](../how-to/file-search.md) tool which is faster and supports more files.
+[Azure AI Studio](https://ai.azure.com) lets you use Assistants v2 which provides several upgrades such as the [file search](../how-to/file-search.md) tool which is faster and supports more files.
 
 1. Sign in to [Azure AI Studio](https://ai.azure.com).
 1. Go to your project or [create a new project](../../../ai-studio//how-to/create-projects.md) in Azure AI Studio.

@@ -55,9 +55,9 @@ To successfully make a call against the Azure OpenAI service, you'll need the fo
 
 |Variable name | Value |
 |--------------------------|-------------|
-| `ENDPOINT`               | This value can be found in the **Keys and Endpoint** section when examining your resource from the Azure portal. Alternatively, you can find the value in **Azure OpenAI Studio** > **Playground** > **View code**. An example endpoint is: `https://docs-test-001.openai.azure.com/`.|
+| `ENDPOINT`               | This value can be found in the **Keys and Endpoint** section when examining your resource from the Azure portal. You can also find the endpoint via the **Deployments** page in Azure AI Studio. An example endpoint is: `https://docs-test-001.openai.azure.com/`.|
 | `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`.|
-| `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 or alternatively under **Management** > **Deployments** in Azure OpenAI Studio.|
+| `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 or via the **Deployments** page in Azure AI Studio.|
 
 Go to your resource in the Azure portal. The **Keys and Endpoint** 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.
 

@@ -27,9 +27,9 @@ To successfully make a call against Azure OpenAI, you'll need the following:
 
 |Variable name | Value |
 |--------------------------|-------------|
-| `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 **Azure OpenAI Studio** > **Playground** > **Code View**. An example endpoint is: `https://docs-test-001.openai.azure.com/`.|
+| `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 Studio. An example endpoint is: `https://docs-test-001.openai.azure.com/`.|
 | `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`.|
-| `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** > **Deployments** in the Azure portal or alternatively under **Management** > **Deployments** in Azure OpenAI Studio.|
+| `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** > **Deployments** in the Azure portal or via the **Deployments** page in Azure AI Studio.|
 
 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.
 

@@ -26,7 +26,7 @@ To successfully make a call against Azure OpenAI, you'll need an **endpoint** an
 
 | Variable name | Value                                                                                                                                                                                                                                                                                    |
 | ------------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
-| `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://docs-test-001.openai.azure.com/`. |
+| `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 Studio. An example endpoint is: `https://docs-test-001.openai.azure.com/`. |
 | `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.

@@ -41,7 +41,7 @@ To successfully call the Azure OpenAI APIs, you need the following information a
 
 | Variable | Name | Value |
 |---|---|---|
-| **Endpoint** | `api_base` | The endpoint value is located under **Keys and Endpoint** for your resource in the Azure portal. Alternatively, you can find the value in **Azure OpenAI Studio** > **Playground** > **Code View**. An example endpoint is: `https://docs-test-001.openai.azure.com/`. |
+| **Endpoint** | `api_base` | The endpoint value is located under **Keys and Endpoint** for your resource in the Azure portal. You can also find the endpoint via the **Deployments** page in Azure AI Studio. An example endpoint is: `https://docs-test-001.openai.azure.com/`. |
 | **Key** | `api_key` | The key value is also located under **Keys and Endpoint** for your resource in the Azure portal. Azure generates two keys for your resource. You can use either value. |
 
 Go to your resource in the Azure portal. On the navigation pane, select **Keys and Endpoint** under **Resource Management**. Copy the **Endpoint** value and an access key value. You can use either the **KEY 1** or **KEY 2** value. Always having two keys allows you to securely rotate and regenerate keys without causing a service disruption.

@@ -40,7 +40,7 @@ To successfully call the Azure OpenAI APIs, you need the following information a
 
 | Variable | Name | Value |
 |---|---|---|
-| **Endpoint** | `api_base` | The endpoint value is located under **Keys and Endpoint** for your resource in the Azure portal. Alternatively, you can find the value in **Azure OpenAI Studio** > **Playground** > **Code View**. An example endpoint is: `https://docs-test-001.openai.azure.com/`. |
+| **Endpoint** | `api_base` | The endpoint value is located under **Keys and Endpoint** for your resource in the Azure portal. You can also find the endpoint via the **Deployments** page in Azure AI Studio. An example endpoint is: `https://docs-test-001.openai.azure.com/`. |
 | **Key** | `api_key` | The key value is also located under **Keys and Endpoint** for your resource in the Azure portal. Azure generates two keys for your resource. You can use either value. |
 
 Go to your resource in the Azure portal. On the navigation pane, select **Keys and Endpoint** under **Resource Management**. Copy the **Endpoint** value and an access key value. You can use either the **KEY 1** or **KEY 2** value. Always having two keys allows you to securely rotate and regenerate keys without causing a service disruption.

@@ -19,7 +19,7 @@ ms.author: mbullwin
 - The following Python libraries: `os`, `json`, `requests`, `openai`.
 - The OpenAI Python library **should be at least version 0.28.1**.
 - Fine-tuning access requires **Cognitive Services OpenAI Contributor**.
-- If you do not already have access to view quota, and deploy models in Azure OpenAI Studio you will require [additional permissions](../how-to/role-based-access-control.md).  
+- If you do not already have access to view quota, and deploy models in Azure AI Studio you will require [additional permissions](../how-to/role-based-access-control.md).  
 
 
 ## Models
@@ -384,7 +384,7 @@ When the fine-tuning job succeeds, the value of the `fine_tuned_model` variable
 
 [!INCLUDE [Fine-tuning deletion](fine-tune.md)]
 
-You can also use [Azure OpenAI Studio](/azure/ai-services/openai/how-to/fine-tuning?tabs=turbo%2Cpython-new&pivots=programming-language-studio#deploy-a-fine-tuned-model) or the [Azure CLI](#deploy-a-model-with-azure-cli) to deploy your customized model.
+You can also use [Azure AI Studio](/azure/ai-services/openai/how-to/fine-tuning?tabs=turbo%2Cpython-new&pivots=programming-language-ai-studio#deploy-a-fine-tuned-model) or the [Azure CLI](#deploy-a-model-with-azure-cli) to deploy your customized model.
 
 > [!NOTE]
 > Only one deployment is permitted for a customized model. An error occurs if you select an already-deployed customized model.
@@ -525,7 +525,7 @@ az cognitiveservices account deployment create
 
 ## Use a deployed customized model
 
-After your custom model deploys, you can use it like any other deployed model. You can use the **Playgrounds** in [Azure OpenAI Studio](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 Studio](https://ai.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.
 
 # [OpenAI Python 1.x](#tab/python-new)
 
@@ -636,7 +636,7 @@ The result file is a CSV file that contains a header row and a row for each trai
 | `full_valid_loss` | The validation loss calculated at the end of each epoch. When training goes well, loss should decrease. |
 |`full_valid_mean_token_accuracy` | The valid mean token accuracy calculated at the end of each epoch. When training is going well, token accuracy should increase. |
 
-You can also view the data in your results.csv file as plots in Azure OpenAI Studio. Select the link for your trained model, and you will see three charts: loss, mean token accuracy, and token accuracy. If you provided validation data, both datasets will appear on the same plot.
+You can also view the data in your results.csv file as plots in Azure AI Studio. Select the link for your trained model, and you will see three charts: loss, mean token accuracy, and token accuracy. If you provided validation data, both datasets will appear on the same plot.
 
 Look for your loss to decrease over time, and your accuracy to increase. If you see a divergence between your training and validation data that can indicate that you are overfitting. Try training with fewer epochs, or a smaller learning rate multiplier.
 
@@ -650,14 +650,14 @@ When you're done with your customized model, you can delete the deployment and m
 
 You can use various methods to delete the deployment for your customized model:
 
-- [Azure OpenAI Studio](../how-to/fine-tuning.md?pivots=programming-language-studio#delete-your-model-deployment)</a>
+- [Azure AI Studio](../how-to/fine-tuning.md?pivots=programming-language-ai-studio#delete-your-model-deployment)</a>
 - The [Azure CLI](/cli/azure/cognitiveservices/account/deployment?preserve-view=true#az-cognitiveservices-account-deployment-delete)
 
 ### Delete your customized model
 
 Similarly, you can use various methods to delete your customized model:
 
-- [Azure OpenAI Studio](../how-to/fine-tuning.md?pivots=programming-language-studio#delete-your-customized-model)
+- [Azure AI Studio](../how-to/fine-tuning.md?pivots=programming-language-ai-studio#delete-your-customized-model)
 
 > [!NOTE]
 > You can't delete a customized model if it has an existing deployment. You must first [delete your model deployment](#delete-your-model-deployment) before you can delete your customized model.
@@ -666,7 +666,7 @@ Similarly, you can use various methods to delete your customized model:
 
 You can optionally delete training and validation files that you uploaded for training, and result files generated during training, from your Azure OpenAI subscription. You can use the following methods to delete your training, validation, and result files:
 
-- [Azure OpenAI Studio](../how-to/fine-tuning.md?pivots=programming-language-studio#delete-your-training-files)
+- [Azure AI Studio](../how-to/fine-tuning.md?pivots=programming-language-ai-studio#delete-your-training-files)
 - The [REST APIs](/rest/api/azureopenai/files/delete)
 - The Python SDK
 
@@ -728,4 +728,4 @@ print(response.model_dump_json(indent=2))
 
 We also recommend including the `suffix` parameter to make it easier to distinguish between different iterations of your fine-tuned model. `suffix` takes a string, and is set to identify the fine-tuned model. With the OpenAI Python API a string of up to 18 characters is supported that will be added to your fine-tuned model name.
 
-If you are unsure of the ID of your existing fine-tuned model this information can be found in the **Models** page of Azure OpenAI Studio, or you can generate a [list of models](/rest/api/azureopenai/models/list?view=rest-azureopenai-2023-12-01-preview&tabs=HTTP&preserve-view=true) for a given Azure OpenAI resource using the REST API.
+If you are unsure of the ID of your existing fine-tuned model this information can be found in the **Models** page of Azure AI Studio, or you can generate a [list of models](/rest/api/azureopenai/models/list?view=rest-azureopenai-2023-12-01-preview&tabs=HTTP&preserve-view=true) for a given Azure OpenAI resource using the REST API.

@@ -17,7 +17,7 @@ ms.author: mbullwin
 - An Azure subscription. <a href="https://azure.microsoft.com/free/cognitive-services" target="_blank">Create one for free</a>.
 - An Azure OpenAI resource. For more information, see [Create a resource and deploy a model with Azure OpenAI](../how-to/create-resource.md).
 - Fine-tuning access requires **Cognitive Services OpenAI Contributor**.
-- If you don't already have access to view quota, and deploy models in Azure OpenAI Studio you'll require [additional permissions](../how-to/role-based-access-control.md).  
+- If you don't already have access to view quota, and deploy models in Azure AI Studio you'll require [additional permissions](../how-to/role-based-access-control.md).  
 
 
 
@@ -344,7 +344,7 @@ az cognitiveservices account deployment create
 
 ## Use a deployed customized model
 
-After your custom model deploys, you can use it like any other deployed model. You can use the **Playgrounds** in [Azure OpenAI Studio](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'll use the Completions playground and the Completions API. For fine-tuned `gpt-35-turbo-0613` models you'll 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 Studio](https://ai.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'll use the Completions playground and the Completions API. For fine-tuned `gpt-35-turbo-0613` models you'll use the Chat playground and the Chat completion API.
 
 ```bash
 curl $AZURE_OPENAI_ENDPOINT/openai/deployments/<deployment_name>/chat/completions?api-version=2023-05-15 \
@@ -381,7 +381,7 @@ The result file is a CSV file that contains a header row and a row for each trai
 | `full_valid_loss` | The validation loss calculated at the end of each epoch. When training goes well, loss should decrease. |
 |`full_valid_mean_token_accuracy` | The valid mean token accuracy calculated at the end of each epoch. When training is going well, token accuracy should increase. |
 
-You can also view the data in your results.csv file as plots in Azure OpenAI Studio. Select the link for your trained model, and you will see three charts: loss, mean token accuracy, and token accuracy. If you provided validation data, both datasets will appear on the same plot.
+You can also view the data in your results.csv file as plots in Azure AI Studio. Select the link for your trained model, and you will see three charts: loss, mean token accuracy, and token accuracy. If you provided validation data, both datasets will appear on the same plot.
 
 Look for your loss to decrease over time, and your accuracy to increase. If you see a divergence between your training and validation data that may indicate that you are overfitting. Try training with fewer epochs, or a smaller learning rate multiplier.
 
@@ -394,14 +394,14 @@ When you're done with your customized model, you can delete the deployment and m
 
 You can use various methods to delete the deployment for your customized model:
 
-- [Azure OpenAI Studio](../how-to/fine-tuning.md?pivots=programming-language-studio#delete-your-model-deployment)
+- [Azure AI Studio](../how-to/fine-tuning.md?pivots=programming-language-ai-studio#delete-your-model-deployment)
 - The [Azure CLI](/cli/azure/cognitiveservices/account/deployment?view=azure-cli-latest&preserve-view=true#az-cognitiveservices-account-deployment-delete)
 
 ### Delete your customized model
 
 Similarly, you can use various methods to delete your customized model:
 
-- [Azure OpenAI Studio](../how-to/fine-tuning.md?pivots=programming-language-studio#delete-your-customized-model)
+- [Azure AI Studio](../how-to/fine-tuning.md?pivots=programming-language-ai-studio#delete-your-customized-model)
 
 > [!NOTE]
 > You can't delete a customized model if it has an existing deployment. You must first [delete your model deployment](#delete-your-model-deployment) before you can delete your customized model.
@@ -410,7 +410,7 @@ Similarly, you can use various methods to delete your customized model:
 
 You can optionally delete training and validation files that you uploaded for training, and result files generated during training, from your Azure OpenAI subscription. You can use the following methods to delete your training, validation, and result files:
 
-- [Azure OpenAI Studio](../how-to/fine-tuning.md?pivots=programming-language-studio#delete-your-training-files)
+- [Azure AI Studio](../how-to/fine-tuning.md?pivots=programming-language-ai-studio#delete-your-training-files)
 
 ## Continuous fine-tuning
 
@@ -432,4 +432,4 @@ curl -X POST $AZURE_OPENAI_ENDPOINT/openai/fine_tuning/jobs?api-version=2023-12-
 
 We also recommend including the `suffix` parameter to make it easier to distinguish between different iterations of your fine-tuned model. `suffix` takes a string, and is set to identify the fine-tuned model. The suffix can contain up to 40 characters (a-z, A-Z, 0-9,- and _) that will be added to your fine-tuned model name.
 
-If you're unsure of the ID of your fine-tuned model this information can be found in the **Models** page of Azure OpenAI Studio, or you can generate a [list of models](/rest/api/azureopenai/models/list?view=rest-azureopenai-2023-12-01-preview&tabs=HTTP&preserve-view=true) for a given Azure OpenAI resource using the REST API.
+If you're unsure of the ID of your fine-tuned model this information can be found in the **Models** page of Azure AI Studio, or you can generate a [list of models](/rest/api/azureopenai/models/list?view=rest-azureopenai-2023-12-01-preview&tabs=HTTP&preserve-view=true) for a given Azure OpenAI resource using the REST API.

@@ -17,7 +17,7 @@ To successfully make a call against Azure OpenAI, you need an **endpoint** and a
 
 |Variable name | Value |
 |--------------------------|-------------|
-| `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://docs-test-001.openai.azure.com/`.|
+| `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 Studio. An example endpoint is: `https://docs-test-001.openai.azure.com/`. |
 | `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 **Keys & Endpoint** section 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.

@@ -26,7 +26,7 @@ To successfully make a call against Azure OpenAI, you need an **endpoint** and a
 
 |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_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 Studio. 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.

@@ -28,7 +28,7 @@ To successfully call the Azure OpenAI APIs, you need the following information a
 
 | Variable | Name | Value |
 |---|---|---|
-| **Endpoint** | `api_base` | The endpoint value is located under **Keys and Endpoint** for your resource in the Azure portal. Alternatively, you can find the value in **Azure OpenAI Studio** > **Playground** > **Code View**. An example endpoint is: `https://docs-test-001.openai.azure.com/`. |
+| **Endpoint** | `api_base` | The endpoint value is located under **Keys and Endpoint** for your resource in the Azure portal. You can also find the endpoint via the **Deployments** page in Azure AI Studio. An example endpoint is: `https://docs-test-001.openai.azure.com/`. |
 | **Key** | `api_key` | The key value is also located under **Keys and Endpoint** for your resource in the Azure portal. Azure generates two keys for your resource. You can use either value. |
 
 Go to your resource in the Azure portal. On the navigation pane, select **Keys and Endpoint** under **Resource Management**. Copy the **Endpoint** value and an access key value. You can use either the **KEY 1** or **KEY 2** value. Having two keys allows you to securely rotate and regenerate keys without causing a service disruption.

@@ -24,9 +24,9 @@ To successfully make a call against the Azure OpenAI service, you'll need the fo
 
 | Variable name     | Value                                                                                                                                                                                                                                                                                |
 | ----------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ |
-| `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 **Azure OpenAI Studio** > **Playground** > **Code View**. An example endpoint is: `https://docs-test-001.openai.azure.com/`. |
+| `ENDPOINT`        | This value can be found in the **Keys & Endpoint** section when examining your resource from the Azure portal. You can also find the endpoint via the **Deployments** page in Azure AI Studio. An example endpoint is: `https://docs-test-001.openai.azure.com/`. |
 | `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`.                                                                                                                                  |
-| `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** > **Deployments** in the Azure portal or alternatively under **Management** > **Deployments** in Azure OpenAI Studio.   |
+| `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** > **Deployments** in the Azure portal or via the **Deployments** page in Azure AI Studio.   |
 
 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.
 

@@ -50,9 +50,9 @@ To successfully make a call against the Azure OpenAI Service, you'll need the fo
 
 |Variable name | Value |
 |--------------------------|-------------|
-| `ENDPOINT`               | This value can be found in the **Keys and Endpoint** section when examining your resource from the Azure portal. Alternatively, you can find the value in **Azure OpenAI Studio** > **Playground** > **View code**. An example endpoint is: `https://docs-test-001.openai.azure.com/`.|
+| `ENDPOINT`               | This value can be found in the **Keys and Endpoint** section when examining your resource from the Azure portal. You can also find the endpoint via the **Deployments** page in Azure AI Studio. An example endpoint is: `https://docs-test-001.openai.azure.com/`.|
 | `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`.|
-| `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 or alternatively under **Management** > **Deployments** in Azure OpenAI Studio.|
+| `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 or via the **Deployments** page in Azure AI Studio.|
 
 Go to your resource in the Azure portal. The **Keys and Endpoint** 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.
 

@@ -24,9 +24,9 @@ To successfully make a call against Azure OpenAI, you'll need the following:
 
 |Variable name | Value |
 |--------------------------|-------------|
-| `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 **Azure OpenAI Studio** > **Playground** > **Code View**. An example endpoint is: `https://docs-test-001.openai.azure.com/`.|
+| `ENDPOINT`               | This value can be found in the **Keys & Endpoint** section when examining your resource from the Azure portal. You can also find the endpoint via the **Deployments** page in Azure AI Studio. An example endpoint is: `https://docs-test-001.openai.azure.com/`.|
 | `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`.|
-| `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** > **Deployments** in the Azure portal or alternatively under **Management** > **Deployments** in Azure OpenAI Studio.|
+| `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** > **Deployments** in the Azure portal or via the **Deployments** page in Azure AI Studio.|
 
 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.
 

@@ -48,7 +48,7 @@ To make requests to your Azure OpenAI service, you need the service endpoint as
 
 ### Get the 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 value in the **Azure OpenAI Studio** > **Playground** > **Code View**. An example endpoint is: `https://aoai-docs.openai.azure.com/`.
+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 Studio. An example endpoint is: `https://docs-test-001.openai.azure.com/`.
 
 :::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":::
 

@@ -40,7 +40,7 @@ To successfully make a call against Azure OpenAI, you need an **endpoint** and a
 
 |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_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 Studio. 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.

@@ -23,7 +23,7 @@ To successfully make a call against Azure OpenAI, you need an **endpoint** and a
 
 |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_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 Studio. 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.

@@ -21,7 +21,7 @@ To successfully make a call against Azure OpenAI, you need an *endpoint* and a *
 
 |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_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 Studio. 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.

@@ -40,7 +40,7 @@ To successfully make a call against Azure OpenAI, you need an *endpoint* and a *
 
 |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_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 Studio. 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.

@@ -25,7 +25,7 @@ To successfully make a call against Azure OpenAI, you need an *endpoint* and a *
 
 |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_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 Studio. 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.

@@ -22,7 +22,7 @@ To successfully make a call against Azure OpenAI, you need an *endpoint* and a *
 
 |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_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 Studio. 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.

@@ -27,7 +27,7 @@ To successfully make a call against Azure OpenAI, you need an *endpoint* and a *
 
 |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_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 Studio. 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.

@@ -168,7 +168,7 @@ To minimize issues related to rate limits, it's a good idea to use the following
 
 ### How to request increases to the default quotas and limits
 
-Quota increase requests can be submitted from the [Quotas](./how-to/quota.md) page of Azure OpenAI Studio. Note that due to overwhelming demand, quota increase requests are being accepted and will be filled in the order they are received. Priority will be given to customers who generate traffic that consumes the existing quota allocation, and your request might be denied if this condition isn't met.
+Quota increase requests can be submitted from the [Quotas](./how-to/quota.md) page of Azure AI Studio. Note that due to overwhelming demand, quota increase requests are being accepted and will be filled in the order they are received. Priority will be given to customers who generate traffic that consumes the existing quota allocation, and your request might be denied if this condition isn't met.
 
 For other rate limits, [submit a service request](../cognitive-services-support-options.md?context=/azure/ai-services/openai/context/context).