@@ -38,7 +38,7 @@ A Conversational Language Understanding (CLU) fine-tuning task is a workspace pr
 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 Foundry.
 1. If you're not already at your project for this task, select it.
-1. Select Fine-tuning from the left navigation panel.
+1. Select Fine-tuning from the left navigation pane.
 
    :::image type="content" source="../media/select-fine-tuning.png" alt-text="Screenshot of fine-tuning selector in the Foundry.":::
 

@@ -6,10 +6,11 @@ author: laujan
 manager: nitinme
 ms.service: azure-ai-language
 ms.topic: how-to
-ms.date: 11/18/2025
+ms.date: 01/11/2026
 ms.author: lajanuar
 ms.custom: language-service-clu
 ---
+
 # Train a conversational language understanding model
 
 After you complete [labeling your utterances](tag-utterances.md), you can start training a model. Training is the process where the model learns from your [labeled utterances](tag-utterances.md). <!--After training is completed, you can [view model performance](view-model-evaluation.md).-->
@@ -91,7 +92,7 @@ Use the evaluation scores to guide your decisions. There may be times where a sp
 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 Foundry.
 1. If you're not already at your project for this task, select it.
-1. Select Fine-tuning from the left navigation panel.
+1. Select Fine-tuning from the left navigation pane.
 
    :::image type="content" source="../media/select-fine-tuning.png" alt-text="Screenshot of fine-tuning selector in the Foundry.":::
 
@@ -105,15 +106,15 @@ Use the evaluation scores to guide your decisions. There may be times where a sp
 
 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.
 
-1. Select the  **Create** button. It may take a few minutes for the create operation to complete.
+1. Select the  **Create** button. It may take a few minutes for the operation to complete.
 
     > [!NOTE]
     >
     > * **Standard training** enables faster training times and quicker iterations; however it's only available for English.
     > * **Advanced training** includes longer training durations and is supported for English, other languages, and multilingual projects.
     > * For more information, *see* [Training modes](#training-modes).
 
-1. From the immediate left navigation panel, choose **Train model**.
+1. From the immediate left navigation pane, choose **Train model**.
 
    :::image type="content" source="../media/train-fine-tuning-model.png" alt-text="Screenshot of the train model selection in the Foundry.":::
 

@@ -17,7 +17,7 @@ Microsoft Foundry offers a unified platform for building, managing, and deployin
 
 Use this article to get started with Conversational Language understanding using Foundry or the REST API.
 
-::: zone pivot="azure-ai-foundry"
+::: zone pivot="microsoft-foundry"
 
 [!INCLUDE [Foundry quickstart](includes/quickstarts/azure-ai-foundry.md)]
 

@@ -6,25 +6,24 @@ author: laujan
 manager: nitinme
 ms.service: azure-ai-language
 ms.topic: quickstart
-ms.date: 11/18/2025
+ms.date: 01/11/2026
 ms.author: lajanuar
 ms.custom: language-service-custom-ner, mode-other
 zone_pivot_groups: foundry-rest-api
 ---
+<!-- markdownlint-disable MD025 -->
 # Quickstart: Custom named entity recognition
 
-This guide provides step-by-step instructions for using custom named entity recognition (NER) with Microsoft Foundry or the REST API. NER lets you detect and categorize entities in unstructured text—like people, places, organizations, and numbers. With custom NER, you can train models to identify entities specific to your business and adapt them as needs evolve.
+This guide provides step-by-step instructions for using custom named entity recognition (NER) with Microsoft Foundry or the REST API. NER lets you detect and categorize entities in unstructured text—like people, places, organizations, and numbers. Custom NER enables the training of models to identify entities that are specific to a business and allows for ongoing adaptation as requirements change.
 
 To get start, [a sample loan agreement](https://go.microsoft.com/fwlink/?linkid=2175226) is provided as a dataset to build a custom NER model and extract these key entities:
 
-*    Date of the agreement
-*    Borrower's name, address, city, and state
-*    Lender's name, address, city, and state
-*    Loan and interest amounts
+* Date of the agreement
+* Borrower's name, address, city, and state
+* Lender's name, address, city, and state
+* Loan and interest amounts
 
-
-
-::: zone pivot="azure-ai-foundry"
+::: zone pivot="microsoft-foundry"
 
 [!INCLUDE [Foundry](includes/quickstarts/azure-ai-foundry.md)]
 

@@ -10,9 +10,7 @@
 ms.custom: include
 ---
 
-## Prerequisites
-
-* **Azure subscription**. If you don't have one, you can [create one for free](https://azure.microsoft.com/pricing/purchase-options/azure-account?cid=msft_learn).
+* **An Azure subscription**. If you don't have one, you can [create one for free](https://azure.microsoft.com/pricing/purchase-options/azure-account?cid=msft_learn).
 * **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).
-*  [Foundry resource](/azure/ai-services/multi-service-resource). For more information, *see* [Configure a Foundry resource](../../../concepts/configure-azure-resources.md). Alternately, you can use a [Language resource](https://portal.azure.com/?Microsoft_Azure_PIMCommon=true#create/Microsoft.CognitiveServicesTextAnalytics).
+* [**A Foundry resource**](/azure/ai-services/multi-service-resource) (recommended). For more information, *see* [Configure a Foundry resource](../../concepts/configure-azure-resources.md). Alternately, you can use a [Language resource](https://portal.azure.com/?Microsoft_Azure_PIMCommon=true#create/Microsoft.CognitiveServicesTextAnalytics).
 * **A Foundry project created in the Foundry**. For more information, *see* [Create a Foundry project](/azure/ai-foundry/how-to/create-projects).

@@ -0,0 +1,119 @@
+---
+author: laujan
+manager: nitinme
+ms.service: azure-ai-language
+ms.topic: include
+ms.date: 01/09/2026
+ms.author: lajanuar
+---
+<!-- markdownlint-disable MD041 -->
+## Prerequisites
+
+> [!NOTE]
+>
+> * If you already have an Azure Language in Foundry Tools or multi-service resource you can continue to use those existing Language resources within the Microsoft Foundry portal via a Foundry Hub project.
+> * For more information, see [How to use Foundry Tools in the Foundry portal](/azure/ai-services/connect-services-ai-foundry-portal).
+> * We highly recommended that you use a Foundry resource in the Foundry; however, you can also follow these instructions using a Language resource.
+
+<!-- markdownlint-disable MD032 -->
+[!INCLUDE [Foundry prerequisites](../../../includes/microsoft-foundry/prerequisites.md)]
+* A [**Conversational language understanding (CQA)**](../../../conversational-language-understanding/overview.md) or [**Custom question answering (CQA)**](../../../question-answering/overview.md) project created in the Foundry.
+
+## Get started
+
+After you create your Foundry resource, you can initiate an orchestration workflow project in the [Microsoft Foundry](https://ai.azure.com/). This project serves as a dedicated workspace for developing custom machine learning models using your data. Access to the project is restricted to you and others who have permissions for the associated Foundry resource.
+
+For this quickstart, you can complete the [**Conversational Language Understanding quickstart**](../../../conversational-language-understanding/quickstart.md) or [**Custom question answering (CQA)**](../../../question-answering/quickstart/sdk.md) to establish a project for use in our subsequent orchestration workflow quickstart steps.
+
+Let's begin:
+
+1. Navigate to the Foundry.
+
+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 Foundry.
+
+1. If you're not already in your **`CLU`** or **`CQA`** project, select it now.
+
+## Create an orchestration workflow project
+
+1. Select **Fine-tuning** from the left navigation pane.
+
+1. From the window that appears, select the AI Service fine-tuning tab and then the **+ Fine-tune** button.
+
+   :::image type="content" source="../../media/select-fine-tuning.png" alt-text="Screenshot of create fine-tuning button in the Foundry.":::
+
+1. From the window that appears, select **Conversational Orchestration Workflow** as the task type, then select **Next**.
+
+   :::image type="content" source="../../media/select-orchestration-workflow.png" alt-text="Screenshot of selecting orchestration workflow in the Foundry.":::
+
+1. In the **Create service fine-tuning window**, you can choose to create a new task or import an existing one. Complete all required fields, then select **Create**:
+
+    * **Name**: Provide a unique name for your orchestration workflow project.
+    * **Language**: Select the language for your project.
+    * **Description**: Optionally, provide a description for your project.
+
+1. After creating the orchestration workflow project, you'll be directed to the project overview page. Here, you can manage your project settings, monitor training progress, and access various tools to enhance your model.
+
+## Link tasks
+
+1. To add existing **`CLU`** or **`CQA`** models to your orchestration workflow, navigate to the **Link tasks** button within your project. Here, you can add intents and entities from your existing models to the orchestration workflow.
+
+   :::image type="content" source="../../media/orchestration-workflow-overview.png" alt-text="Screenshot of orchestration workflow overview in the Foundry." lightbox="../../media/orchestration-workflow-overview.png":::
+
+1. Link your tasks by selecting from the **Task type** and **Fine-tuning task name** dropdown menus. The **Intent name** field automatically populates with the same name as the **fine-tuning task name** field. Once everything is set, select **Add** to continue.
+
+   :::image type="content" source="../../media/orchestration-link-tasks.png" alt-text="Screenshot of linking tasks in orchestration workflow in the Foundry.":::
+
+## Add training data
+
+1. Navigate to the Manage Data tab and add your utterances file. For this project, you can download our [**sample utterances file**](https://github.com/Azure-Samples/cognitive-services-sample-data-files/blob/master/language-service/CLU/clu_utterances.json) which comes preconfigured with labeled utterances.
+
+1. After uploading your utterances file, select the **unlinked intents** from the Insights pane. This action allows you to map these intents to the appropriate linked tasks within your orchestration workflow.
+
+   :::image type="content" source="../../media/select-unlinked-intents.png" alt-text="Screenshot of unlinked intents in orchestration workflow in the Foundry." lightbox="../../media/select-unlinked-intents.png":::
+
+## Train your model
+
+1. Navigate to the **Train model** section and select the **Train model** button to start training your orchestration workflow with the linked tasks and uploaded utterances. This process may take some time depending on the size of your dataset and the complexity of your model.
+
+   :::image type="content" source="../../media/train-orchestration-model.png" alt-text="Screenshot of training orchestration button in the Foundry.":::
+
+1. In the **Train a new model** window, provide a name for your model, keep the default standard training mode, and select **Next** to proceed.
+
+   :::image type="content" source="../../media/train-new-model.png" alt-text="Screenshot of training orchestration model window in the Foundry.":::
+
+1. In the data splitting window, you can choose to either use the default data split or customize it according to your needs. After making your selection, select **Next** to continue.
+
+1. Review your selections in the summary window, and if everything looks correct, select **Create** to initiate the training process for your orchestration workflow model.
+
+   :::image type="content" source="../../media/training-review.png" alt-text="Screenshot of orchestration model training summary in the Foundry.":::
+
+1. After initiating the training process, you can monitor the progress and view detailed metrics on the training dashboard. Once the training is complete, your orchestration workflow model is ready for deployment and testing.
+
+## Deploy your model
+
+Deploy your trained model by navigating to the **Deploy model** section and selecting the **Deploy** button. Follow the prompts to complete the deployment process.
+
+   :::image type="content" source="../../media/deploy-trained-model.png" alt-text="Screenshot of deploying orchestration model button in the Foundry.":::
+
+## Test your model
+
+After your model successfully deploys, you can test it directly within the Foundry interface. Navigate to the **Test in playground** section, input various utterances, and observe how your orchestration workflow routes requests to the appropriate linked tasks.
+
+That's it, congratulations!
+
+## Clean up resources
+
+If you no longer need your project, you can delete it from the Foundry.
+
+1. Navigate to the [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 Foundry**
+
+1. Select **Management center**.
+
+1. Select **Delete project**.
+
+
+

@@ -6,18 +6,28 @@ author: laujan
 manager: nitinme
 ms.service: azure-ai-language
 ms.topic: quickstart
-ms.date: 01/07/2026
+ms.date: 01/09/2026
 ms.author: lajanuar
 ms.custom: language-service-clu, mode-other
+zone_pivot_groups: foundry-rest-api
 ---
 <!-- markdownlint-disable MD025 -->
-
 # Quickstart: Orchestration workflow
 
-Use this article to get started with Orchestration workflow projects using the REST API. Follow these steps to try out an example.
+Orchestration workflow, offered by [Azure Language in Foundry Tools](../overview.md), enables you to integrate multiple language models—such as Conversational Language Understanding (CLU) and Custom question answering (CQA)—into a single project. This functionality intelligently routes user requests to the most suitable model via a unified endpoint, providing seamless and sophisticated conversational experiences across various language services tasks.
+
+This quickstart walks you through the essentials of working with orchestration workflow projects Following each step builds a strong foundation in the core concepts. Completing this quickstart provides you with hands-on experience, preparing you to confidently tackle orchestration workflow projects in your own environment.
+
+::: zone pivot="microsoft-foundry"
+[!INCLUDE [Microsoft Foundry quickstart](includes/quickstarts/microsoft-foundry.md)]
+:::zone-end
+
+:::zone pivot="rest-api"
 
 [!INCLUDE [REST API quickstart](includes/quickstarts/rest-api.md)]
 
+:::zone-end
+
 ## Next steps
 
 * [Learn about orchestration workflows](overview.md)

@@ -86,11 +86,11 @@ Foundry offers a unified platform where you can easily build, manage, and deploy
 
 ## Step 3: Create a fine-tuning task with connected resources
 
-1. Navigate to **Go to project** at the end of the left navigation panel.
+1. Navigate to **Go to project** at the end of the left navigation pane.
 
    :::image type="content" source="../media/configure-resources/go-to-project.png" alt-text="Screenshot the go-to-project button in the Foundry.":::
 
-1. Select **Fine-tuning** from the left navigation panel, choose the **AI Service fine-tuning** tab, and then select the **+ Fine-tune** button.
+1. Select **Fine-tuning** from the left navigation pane, choose the **AI Service fine-tuning** tab, and then select the **+ Fine-tune** button.
 
    :::image type="content" source="../media/configure-resources/fine-tuning.png" alt-text="Screenshot of the fine tuning selector in the Foundry.":::