@@ -17,9 +17,9 @@ A Conversational Language Understanding (CLU) fine-tuning task is a workspace pr
 
 > [!NOTE]
 >
-> If you already have an Azure AI Language or multi-service resource—whether used on its own or through Language Studio—you can continue to use those existing Language resources within the Azure AI Foundry portal. For more information, see [How to use Azure AI services in the Azure AI Foundry portal](../../../../ai-services/connect-services-ai-foundry-portal.md).
->
-> In Azure AI Foundry, you set up a fine-tuning task to serve as your workspace when customizing your CLU model. Previously, a **fine-tuning task** was referred to as a **CLU project**. You might encounter both terms used interchangeably in older CLU documentation.
+> * If you already have an Azure AI Language or multi-service resource—whether used on its own or through Language Studio—you can continue to use those existing Language resources within the Azure AI Foundry portal. For more information, see [How to use Azure AI services in the Azure AI Foundry portal](../../../../ai-services/connect-services-ai-foundry-portal.md).
+> * In Azure AI Foundry, a fine-tuning task serves as your workspace when customizing your CLU model. Previously, a **fine-tuning task** was referred to as a **CLU project**. You might encounter both terms used interchangeably in older CLU documentation.
+> * We highly recommend that you use an Azure AI Foundry resource in the AI Foundry; however, you can also follow these instructions using a Language resource.
 >
 
 ## Prerequisites
@@ -29,10 +29,6 @@ A Conversational Language Understanding (CLU) fine-tuning task is a workspace pr
 *  An [Azure AI Foundry multi-service resource](../../../multi-service-resource.md). For more information, *see* [Configure an Azure AI Foundry resource](configure-azure-resources.md#option-1-configure-an-azure-ai-foundry-resource). Alternately, you can use an [Azure AI Language resource](https://portal.azure.com/?Microsoft_Azure_PIMCommon=true#create/Microsoft.CognitiveServicesTextAnalytics).
 * A Foundry project created in the Azure AI Foundry. For more information, *see* [Create an AI Foundry project](../../../../ai-foundry/how-to/create-projects.md).
 
-> [!NOTE]
->
-> We highly recommend that you use an Azure AI Foundry resource in the AI Foundry; however, you can also follow these instructions using a Language resource.
-
 ## Create a CLU fine-tuning task project
 
  To create a CLU fine-tuning task project, you first configure your environment and then create a fine-tuning task, which serves as your workspace for customizing your CLU model.
@@ -71,7 +67,7 @@ A Conversational Language Understanding (CLU) fine-tuning task is a workspace pr
 
 1. From **Create service fine-tuning** window, choose the **Conversational language understanding** tab then select **Next**.
 
-    :::image type="content" source="../media/select-project.png" alt-text="Screenshot of conversational language understanding tab in the Azure AI Foundry.":::
+    :::image type="content" source="../media/select-project.png" alt-text="Screenshot of conversational language understanding selection card in the Azure AI Foundry.":::
 
 1. In **Create CLU fine tuning task** window, select your **Connected service** from the drop-down menu, then complete the **Name** and **Language** fields. If you're using the free **Standard Training** mode, select **English** for the language field.
 
@@ -84,7 +80,7 @@ A Conversational Language Understanding (CLU) fine-tuning task is a workspace pr
    > * **Advanced training** includes longer training durations and is supported for English, other languages, and multilingual projects.
    > * For more information, *see* [Training modes](train-model.md#training-modes).
 
-1. Once the task creation is complete, select the task from the AI Service fine-tuning window to arrive at the Getting started with fine-tuning page.
+1. Once the task creation is complete, select the task from the AI Service fine-tuning window to arrive at the **Getting started with fine-tuning** page.
 
    :::image type="content" source="../media/create-project/getting-started-fine-tuning.png" alt-text="Screenshot of the getting started with fine-tuning page in the Azure AI Foundry." lightbox="../media/create-project/getting-started-fine-tuning.png":::
 

@@ -54,9 +54,10 @@ We recommend that you introduce casing and punctuation diversity in the training
 
 ## Data splitting
 
-Before you start the training process, labeled utterances in your project are divided into a training set and a testing set. Each one of them serves a different function.
-The **training set** is used in training the model, the set from which the model learns the labeled utterances.
-The **testing set** is a blind set that isn't introduced to the model during training but only during evaluation.
+Before you start the training process, labeled utterances in your project are divided into a training set and a testing set. Each one of them serves a different function:
+
+* The **training set** is used in training the model, the set from which the model learns the labeled utterances.
+* The **testing set** is a blind set that isn't introduced to the model during training but only during evaluation.
 
 After the model is trained successfully, the model can be used to make predictions from the utterances in the testing set. These predictions are used to calculate [evaluation metrics](../concepts/evaluation-metrics.md).
 We recommend that you make sure that all your intents and entities are adequately represented in both the training and testing set.

@@ -0,0 +1,158 @@
+---
+author: laujan
+manager: nitinme
+ms.service: azure-ai-language
+ms.topic: include
+ms.date: 08/03/2025
+ms.author: lajanuar
+---
+
+Azure AI Foundry offers a unified platform for building, managing, and deploying AI solutions with a wide array of models and tools. Azure AI Foundry playgrounds are interactive environments within the Azure AI Foundry portal designed for exploring, testing, and prototyping with various AI models and tools.
+
+Use this article to get started with Conversational Language understanding using Azure AI Foundry or the REST API.
+
+> [!NOTE]
+>
+> * If you already have an Azure AI Language or multi-service resource—whether used on its own or through Language Studio—you can continue to use those existing Language resources within the Azure AI Foundry portal. 
+> * For more information, see [How to use Azure AI services in the Azure AI Foundry portal](/azure/ai-services/connect-services-ai-foundry-portal).
+> * We highly recommended that you use an Azure AI Foundry resource in the AI Foundry; however, you can also follow these instructions using a Language resource.
+
+## Prerequisites
+
+* **Azure subscription**. If you don't have one, you can [create one for free](https://azure.microsoft.com/free/cognitive-services).
+* **Requisite permissions**. Make sure the person establishing the account and project is assigned as the Azure AI Account Owner role at the subscription level. Alternatively, having either the **Contributor** or **Cognitive Services Contributor** role at the subscription scope also meets this requirement. For more information, *see* [Role based access control (RBAC)](/azure/ai-foundry/openai/how-to/role-based-access-control#cognitive-services-contributor).
+*  [Azure AI Foundry multi-service resource](/azure/ai-services/multi-service-resource). For more information, *see* [Configure an Azure AI Foundry resource](../../how-to/configure-azure-resources.md#option-1-configure-an-azure-ai-foundry-resource). Alternately, you can use an [Azure AI Language resource](https://portal.azure.com/?Microsoft_Azure_PIMCommon=true#create/Microsoft.CognitiveServicesTextAnalytics).
+* Foundry project created in the Azure AI Foundry. For more information, *see* [Create an AI Foundry project](/azure/ai-foundry/how-to/create-projects).
+
+## Get started with Azure AI Foundry
+
+To complete this quickstart, you need a Conversational Language Understanding (CLU) fine-tuning task project that includes a [defined schema](../../how-to/build-schema.md) and [labeled utterances](../../how-to/tag-utterances.md). 
+
+You can download our [**sample project file**](https://github.com/Azure-Samples/cognitive-services-sample-data-files/blob/master/language-service/CLU/EmailAppDemo.json), which comes preconfigured with both a schema and labeled utterances. This project enables the prediction of user intent for commands such as reading emails, deleting emails, and attaching documents to emails.
+
+Let's begin:
+
+1. Navigate to the [Azure AI Foundry](https://ai.azure.com/).
+1. If you aren't already signed in, the portal prompts you to do so with your Azure credentials.
+1. Once signed in, you can create or access your existing projects within Azure AI Foundry.
+1. If you're not already at your project for this task, select it.
+1. On the left side navigation pane, select **Playgrounds**, navigate to the **Language playground card**, and then choose the **Try the Language playground** button.
+
+  :::image type="content" source="../../media/quickstarts/try-playground.png" alt-text="Screenshot of the Try Language Playground selection in Azure AI Foundry.":::
+
+## Try the Language playground
+
+The top section of the Language playground is where you can view and select the available Language services. 
+
+1. Select the **Conversational language understanding** card.
+
+    :::image type="content" source="../../media/quickstarts/language-playground.png" alt-text="Screenshot of the language playground homepage in Azure AI Foundry.":::
+
+1. Next, scroll to and select the **Fine-tune** button.
+
+   :::image type="content" source="../../media/quickstarts/fine-tune-button.png" alt-text="Screenshot of the fine-tune button on the language playground homepage in Azure AI Foundry.":::
+
+1. From **Create service fine-tuning** window, choose the **Conversational language understanding** card. Then select **Next**.
+
+    :::image type="content" source="../../media/quickstarts/select-project.png" alt-text="Screenshot of conversational language understanding selection card in the Azure AI Foundry.":::
+
+1. In **Create CLU fine tuning task** window, select **Import an existing project**, then choose your **Connected service** from the drop-down menu and complete the **Name** field.
+
+    :::image type="content" source="../../media/quickstarts/select-import-existing-project.png" alt-text="Screenshot of the import an existing project selection in Azure AI Foundry.":::
+
+1. Next, add the [sample project file](https://github.com/Azure-Samples/cognitive-services-sample-data-files/blob/master/language-service/CLU/EmailAppDemo.json) that you downloaded earlier to the upload area.
+
+1. Select the  **Create** button. It can take a few minutes for the *creating* operation to complete.
+
+1. Once your fine-tuning task project is created, the **Getting started with fine-tuning** page opens.
+
+   :::image type="content" source="../../media/create-project/getting-started-fine-tuning.png" alt-text="Screenshot of the getting started with fine-tuning page in the Azure AI Foundry." lightbox="../../media/create-project/getting-started-fine-tuning.png":::
+
+## Train your model
+
+After project creation, the next steps are [schema construction](../../how-to/build-schema.md) and [utterance labeling](../../how-to/tag-utterances.md). For this quickstart, these steps are preconfigured in the sample project. Therefore, you can go ahead and initiate a training job by selecting **Train model** from the **Getting Started** menu to generate your model.
+
+  :::image type="content" source="../../media/quickstarts/getting-started-menu.png" alt-text="Screenshot of the getting started with fine-tuning menu in the Azure AI Foundry.":::
+
+1. Select the **➕Train model button** from the **Train your model** window.
+
+    :::image type="content" source="../../media/quickstarts/train-your-model-button.png" alt-text="Screenshot of the train your model button in the Azure AI Foundry.":::
+
+1. Complete the **Select a mode** form by completing the **Model name** field and selecting a **Training mode**. For this quickstart, select the free **Standard training** mode. For more information, *see* [Training modes](../../how-to/train-model.md#training-modes).
+
+1. Choose a **Training version** from the drop-down menu, then select the **Next** button.
+
+1. Check your selections in the **Review** window, then select the **Create** button
+
+    :::image type="content" source="../../media/quickstarts/review-selections.png" alt-text="Screenshot of the review selections window in the Azure AI Foundry.":::
+
+## Deploy model
+
+Typically, after training a model, you review its evaluation details. For this quickstart, you can just deploy your model and make it available to test in the Language playground, or by calling the [prediction API](https://aka.ms/clu-apis). However, if you wish, you can take a moment to select **Evaluate your model** from the left-side menu and explore the in-depth telemetry for your model. Complete the following steps to deploy your model within Azure AI Foundry:
+
+1. Select **Deploy model** from the left-side menu.
+1. Next, select **➕Deploy a trained model** from the **Deploy your model** window.
+
+    :::image type="content" source="../../media/quickstarts/deploy-trained-model.png" alt-text="Screenshot of the deploy your model window in Azure AI Foundry.":::
+
+1. Make sure the **Create a new deployment** button is selected.
+
+1. Complete the **Deploy a trained model** window fields:
+
+   * Create a deployment name. 
+   * Select your trained model from the **Assign a model** drop-down menu.
+   * Select a subscription from the **Subscription** drop-down menu.
+   * Select a region from the **Region** drop-down menu.
+   * Select a resource from the **Resource** drop-down menu. The resource must be in the same deployment region.
+
+
+       :::image type="content" source="../../media/quickstarts/deploy-model-configuration.png" alt-text="Screenshot of the deploy your model configuration in Azure AI Foundry.":::
+
+1. Finally, select the **Create** button. It may take a few minutes for your model to deploy.
+
+1. After successful deployment, you can view your model's deployment status on the **Deploy your model** page. The expiration date that appears marks the date when your deployed model becomes unavailable for prediction tasks. This date is usually 18 months after a training configuration is deployed.
+
+    :::image type="content" source="../../media/quickstarts/deployed-model-succeeded.png" alt-text="Screenshot of your successfully deployed model status page in Azure AI Foundry.":::
+
+1. From the far-left menu, navigate to the Language playground.<br>
+   **Playgrounds** → **Language playground (Try the Language playground)**.
+1. Select the **Conversational language understanding** card.
+1. A **Configuration** window with your deployed model should appear in the main/center window.
+1. In the text box, enter an utterance to test. For example, if you used our [sample project](https://github.com/Azure-Samples/cognitive-services-sample-data-files/blob/master/language-service/CLU/EmailAppDemo.json) application for email-related utterances you could enter ***Check email***.
+1. After you enter your test text, select the **Run** button.
+
+    :::image type="content" source="../../media/quickstarts/deployed-model-succeeded.png" alt-text="Screenshot of your successfully deployed model status page in Azure AI Foundry.":::
+1. After you run the test, you should see the response of the model in the result. 
+
+    :::image type="content" source="../../media/quickstarts/language-playground-test.png" alt-text="Screenshot of deployed model testing in Azure AI Foundry language playground.":::
+
+1. You can view the results in a text or JSON format view.
+
+    :::image type="content" source="../../media/quickstarts/language-playground-test-results.png" alt-text="Screenshot of deployed model test results in Azure AI Foundry language playground.":::
+
+That's it, congratulations!
+
+In this quickstart, you deployed a CLU model and tested it in the Azure AI Foundry Language playground. Next, learn how to [Create your own fine-tuning task project ](../../how-to/create-project.md) for your applications and workflows.
+
+## Clean up resources
+
+If you no longer need your project, you can delete it from the Azure AI Foundry.
+
+1. Navigate to the [Azure AI Foundry](https://ai.azure.com/) home page. Initiate the authentication process by signing in, unless you already completed this step and your session is active.
+1. Select the project that you want to delete from the **Keep building with Azure AI Foundry**
+1. Select **Management center**.
+1. Select **Delete project**.
+
+   :::image type="content" source="../../media/create-project/delete-project.png" alt-text="Screenshot of the Delete project button in the Azure AI Foundry.":::
+
+To delete the hub along with all its projects:
+
+1. Navigate to the **Overview** tab inn the **Hub** section.
+
+   :::image type="content" source="../../media/create-project/hub-details.png" alt-text="Screenshot of the hub details list in the Azure AI Foundry.":::
+
+1. On the right, select **Delete hub**.
+1. The link opens the Azure portal for you to delete the hub there.
+
+   :::image type="content" source="../../media/create-project/delete-hub.png" alt-text="Screenshot of the Delete hub button in the Azure AI Foundry.":::
+

@@ -1,4 +1,4 @@
----
+<!-- ---
 author: laujan
 manager: nitinme
 ms.service: azure-ai-language
@@ -46,4 +46,4 @@ Generally after training a model you would review its evaluation details. In thi
 
 ## Clean up resources
 
-[!INCLUDE [Delete project using Language studio](../language-studio/delete-project.md)]
+[!INCLUDE [Delete project using Language studio](../language-studio/delete-project.md)] -->

@@ -3,10 +3,12 @@ author: laujan
 manager: nitinme
 ms.service: azure-ai-language
 ms.topic: include
-ms.date: 05/23/2025
+ms.date: 08/14/2025
 ms.author: lajanuar
 ---
 
+Use this article to get started with Conversational Language Understanding (CLU) using Azure AI Foundry or the REST API.
+
 ## Prerequisites
 
 * Azure subscription - [Create one for free](https://azure.microsoft.com/free/cognitive-services).
@@ -20,13 +22,13 @@ ms.author: lajanuar
 ## Get your resource keys and endpoint
 
 1. Go to your resource overview page in the [Azure portal](https://portal.azure.com/#home).
-2. From the menu on the left side, select **Keys and Endpoint**. You will use the endpoint and key for the API requests 
+2. From the menu on the left side, select **Keys and Endpoint**. You need the endpoint and key for API requests.
 
     :::image type="content" source="../../../media/azure-portal-resource-credentials.png" alt-text="A screenshot showing the key and endpoint page in the Azure portal" lightbox="../../../media/azure-portal-resource-credentials.png":::
 
 ## Import a new CLU sample project
 
-Once you have a Language resource created, create a conversational language understanding project. A project is a work area for building your custom ML models based on your data. Your project can only be accessed by you and others who have access to the Language resource being used.
+Once you have a Language resource created, create a conversational language understanding project. A project is a work area for building your custom ML models based on your data. Only you and others who have access to the language resource being used can access your project.
 
 For this quickstart, you can download [this sample project](https://go.microsoft.com/fwlink/?linkid=2196152) and import it. This project can predict the intended commands from user input, such as: reading emails, deleting emails, and attaching a document to an email. 
 
@@ -54,15 +56,15 @@ Typically, after you create a project, you should [build schema](../../how-to/bu
 
 ### Get the training job status
 
-Training may take time to complete - sometimes between 10 and 30 minutes. You can use the following request to keep polling the status of the training job until it is successfully completed.
+Training may take time to complete - sometimes between 10 and 30 minutes. You can use the following request to keep polling the status of the training job until it successfully completes.
 
  [!INCLUDE [get training model status](../rest-api/get-training-status.md)]
 
 
 
 ## Deploy your model
 
-Generally after training a model you would review its evaluation details. In this quickstart, you will just deploy your model, and call the [prediction API](https://aka.ms/clu-apis) to query the results.
+Generally after training a model you would review its evaluation details. In this quickstart, you deploy your model and call the [prediction API](https://aka.ms/clu-apis) to query the results.
 
 ### Submit deployment job
 

@@ -9,16 +9,14 @@ ms.topic: quickstart
 ms.date: 05/01/2025
 ms.author: lajanuar
 ms.custom: language-service-clu, mode-other
-zone_pivot_groups: usage-custom-language-features
+zone_pivot_groups: language-clu-quickstart
 ---
 
 # Quickstart: Conversational language understanding 
 
-Use this article to get started with Conversational Language understanding using Azure AI Foundry and the REST API. Follow these steps to try out an example.
+::: zone pivot="azure-ai-foundry"
 
-::: zone pivot="language-studio"
-
-[!INCLUDE [Language Studio quickstart](includes/quickstarts/language-studio.md)]
+[!INCLUDE [Azure AI Foundry quickstart](includes/quickstarts/azure-ai-foundry.md)]
 
 ::: zone-end
 
@@ -30,4 +28,6 @@ Use this article to get started with Conversational Language understanding using
 
 ## Next steps
 
-* [Learn about entity components](concepts/entity-components.md)
+* [Build a fine-tuning schema](how-to/build-schema.md)
+* [Label utterances](how-to/tag-utterances.md)
+* [Train a model](how-to/train-model.md)
\ No newline at end of file

@@ -111,7 +111,7 @@ items:
       - name: Service limits
         href: custom-text-classification/service-limits.md
         displayName: quotas, restrictions, service boundaries
-  - name: Conversational language understanding
+  - name: "Conversational language understanding (CLU)"
     items:
     - name: Overview
       href: conversational-language-understanding/overview.md
@@ -127,7 +127,7 @@ items:
       displayName: frequently asked questions, troubleshooting, common issues
     - name: Responsible use of AI
       items:
-      - name: Transparency note for conversational language understanding
+      - name: Transparency note for CLU
         href: /legal/cognitive-services/language-service/clu-transparency-note?context=/azure/ai-services/language-service/context/context
       - name: Integration and responsible use
         href: /legal/cognitive-services/language-service/guidance-integration-responsible-use?context=/azure/ai-services/language-service/context/context
@@ -466,7 +466,7 @@ items:
           href: conversational-language-understanding/concepts/app-architecture.md
     - name: Tutorials
       items:
-        - name: Connect conversational language understanding and custom question answering
+        - name: Connect CLU and custom question answering
           href: orchestration-workflow/tutorials/connect-services.md
           displayName: clu integration, qna integration, orchestration workflow, connect services
     - name: Reference