Update use-ai-foundry.md

kbrowne8 · web-flow · commit 0c0452e36d58 · 2025-05-29T09:45:27.000-07:00
diff --git a/articles/ai-services/content-understanding/quickstart/use-ai-foundry.md b/articles/ai-services/content-understanding/quickstart/use-ai-foundry.md
@@ -1,24 +1,24 @@
 ---
-title: "Use Azure AI Content Understanding Analyzer templates in the Azure AI Foundry Portal"
+title: "Create an Azure AI Content Understanding single-file task in the Azure AI Foundry portal"
 titleSuffix: Azure AI services
-description: Learn how to use Content Understanding Analyzer templates in Azure AI Foundry portal
+description: Create an Azure AI Content Understanding single-file task in the Azure AI Foundry portal
 author: laujan
 manager: nitinme
 ms.service: azure-ai-content-understanding
 ms.topic: quickstart
 ms.date: 05/19/2025
 ---
 
-# Use Azure AI Content Understanding in the Azure AI Foundry portal
+# Create an Azure AI Content Understanding single-file task in the Azure AI Foundry portal
 
-In this quickstart, you will learn how to use the Content Understanding service in the Azure AI Foundry portal. The Azure AI Foundry is a comprehensive platform for developing and deploying generative AI applications and APIs responsibly. 
+In this quickstart, you will learn how to use the Content Understanding service in the Azure AI Foundry portal to create a single-file task which will allow you to generate structured outputs from your video, audio, image and document data. The Azure AI Foundry is a comprehensive platform for developing and deploying generative AI applications and APIs responsibly. 
 
 A few terms to know before getting started:
 * **Task**: Your Content Understanding task is the top-level structure that all of your Content Understanding related work falls under. This guide will offer a step by step introduction to creating your field schema.
-* **Field schema**: A field schema is the definition of all of the outputs that you want to extract or generate data. Content Understanding offers several prebuilt schemas, and are fully customizable. You can also create a schema from scratch to extract exactly what you need.
+* **Field schema**: A field schema is the definition of all of the outputs that you want to extract or generate from your data. Content Understanding offers several prebuilt schemas and they are all fully customizable to meet your business needs. This quickstart will offer guidance to help you build the schema that is right for your scenario.
 * **Analyzer**: The Content Understanding analyzer allows you to call the field schema you define as an API call in your own solution. You can build as many analyzers as needed within your task.
 
-This guide shows you how to build and test a Content Understanding analyzer in the AI Foundry. You can then utilize the extracted data in any app or process you build using a simple REST API call. Content Understanding analyzers are fully customizable. You can create an analyzer by building your own schema from scratch or by using a suggested analyzer template offered to address common scenarios across each data type.
+This guide will show you how to build and test a Content Understanding analyzer in the AI Foundry. You can then utilize the analyzer in any app or process you build using a simple REST API call, allowing you to extract meaningful outputs on your data at scale. Content Understanding analyzers are fully customizable. You can create an analyzer by building your own schema from scratch or by using a suggested analyzer template offered to address common scenarios across each data type.
 
 :::image type="content" source="../media/quickstarts/ai-foundry-overview.png" alt-text="Screenshot of the Content Understanding workflow in the Azure AI Foundry.":::
 
@@ -28,12 +28,10 @@ To get started, make sure you have the following resources and permissions:
 
 * An Azure subscription. If you don't have an Azure subscription, [create a free account](https://azure.microsoft.com/free/).
 
-* An [Azure AI Foundry project](../../../ai-foundry/how-to/create-projects.md) created in one of the following supported regions: `westus`, `swedencentral`, or `australiaeast`. A project is used to organize your work and save state while building customized AI apps.
+* An [Azure AI Foundry hub-based project](../../../ai-foundry/how-to/create-projects.md) created in one of the following supported regions: `westus`, `swedencentral`, or `australiaeast`. A project is used to organize your work and save state while building customized AI apps. You can create a project from the home page of AI Foundry, or the [Content Understanding landing page](aka.ms/cu-landing).
 
 [!INCLUDE [hub based project required](../../../ai-foundry/includes/uses-hub-only.md)]
 
-* If your organization requires you to customize the security of storage resources, refer to [Azure AI services API access keys](../../../ai-foundry/concepts/encryption-keys-portal.md) to create resources that meet your organizations requirements through the Azure portal. To learn how to utilize customer managed keys, refer to [Encrypt data using customer-managed keys](../../../ai-foundry/concepts/encryption-keys-portal.md). 
-
 ## Create a custom task
 
 Follow these steps to create a custom task in the Azure AI Foundry. This task will be used to build your first analyzer.
@@ -42,21 +40,19 @@ Follow these steps to create a custom task in the Azure AI Foundry. This task wi
 1. Select your hub based project. You might need to select **View all resources** to see your project.
 1. Select **Content Understanding** from the left navigation pane.
 1. Select **+ Create**.
-2. Choose a task type. The type of task that you create depends on what data you plan to bring in.
-* [Single-file task:](#single-file-task-standard-mode) A single-file task utilizes Content Understanding Standard mode and allows you to bring in one file to create your analyzer:
-* [Multi-file task:](#multi-file-task-pro-mode) A multi-file task utilizes Content Understanding Pro mode and allows you to bring in multiple files to create your analyzer. You can also bring in a set of reference data that the service can use to perform multi-step reasoning and make conclusions about your data. To learn more about the difference between Content Understanding Standard and Pro mode, check out [Azure AI Content Understanding pro and standard modes](../concepts/standard-pro-modes.md).
+2. In this guide, you will select a single-file task utilizing Content Understanding Standard mode, but if you're interested in creating a multi-file task utilizing Pro mode, refer to [Create an Azure AI Content Understanding multi-file task in the Azure AI Foundry portal
+](./use-ai-foundry-pro-mode.md). For more information on which mode is right for your scenario, check out [Azure AI Content Understanding pro and standard modes](../concepts/standard-pro-modes.md).
 1. Enter a name for your task. Optionally, enter a description and change other settings.
 1. Select **Create**.
 
-## Create your first task analyzer
+## Create your first analyzer
 
-Now that everything is configured to get started, we can walk through, step-by-step, how to build your first analyzer. 
+Now that everything is configured to get started, we can walk through how to build your first analyzer. 
 
-# [Single-file task (Standard mode](#tab/standard)
+## Create your single-file task powered by Content Understanding Standard mode
 
 When you create a single-file Content Understanding task, you'll start by uploading a sample of your data and building your field schema. The schema is the customizable framework that allows the analyzer to extract insights from your data. In this example, the schema is created to extract key data from an invoice document, but you can bring in any type of data and the steps remain the same. [Compare the output of this invoice analysis use case to the output of a Content Understanding Pro invoice analysis scenario](). For a complete list of supported file types, see [input file limits](../service-limits.md#input-file-limits).
 
-
 1. Upload a sample file of an invoice document or any other data relevant to your scenario.
 
    :::image type="content" source="../media/quickstarts/upload-data.png" alt-text="Screenshot of upload step in user experience.":::
@@ -93,60 +89,12 @@ When you create a single-file Content Understanding task, you'll start by upload
 
 Now you successfully built your first Content Understanding analyzer, and are ready to start extracting insights from your data. Check out [Quickstart: Azure AI Content Understanding REST APIs](./use-rest-api.md) to utilize the REST API to call your analyzer.
 
-# [Multi-file task (Pro mode](#tab/pro)
-
-When you create a multi-file Content Understanding task, you'll start by building your field schema. The schema is the customizable framework that guides the analyzer to extract the preferred insights from your data.
-
-In this example, the schema is created to extract key fields from an invoice document, but you can bring in any document based data and the steps remain the same. For a complete list of supported file types, see [input file limits](../service-limits.md#input-file-limits).
-
-1. Upload one or multiple sample files of invoice documents or any other document data relevant to your scenario.
-
-   :::image type="content" source="../media/quickstarts/upload-test-data.png" alt-text="Screenshot of upload step in user experience.":::
-
-2. Add fields to your schema:
-
-    * Specify clear and simple field names. Some example fields might include **vendorName**, **items**, **price**.
-
-    * Indicate the value type for each field (strings, dates, numbers, lists, groups). To learn more, *see* [supported field types](../service-limits.md#field-schema-limits).
-
-    * *[Optional]* Provide field descriptions to explain the desired behavior, including any exceptions or rules.
-
-    * Specify the method to generate the value for each field.
-  
-   :::image type="content" source="../media/quickstarts/add-fields.png" alt-text="Screenshot of upload step in user experience.":::
-
-  
-3. Once you feel that the schema is ready to test, select **Save**. You can always come back and make changes if needed.
-
-   :::image type="content" source="../media/quickstarts/save-schema.png" alt-text="Screenshot of completed schema.":::
-
-4. Upload one or more documents for reference data for the service to analyze. Adding reference data allows the model to compare and apply multi-step reasoning to your test data in order to infer conclusions about that data.
-
-   :::image type="content" source="../media/quickstarts/reference-data.png" alt-text="Screenshot of completed schema.":::
-
-5.  Run analysis on your data. Kicking off analysis generates an output on your test files based on the schema that you just created, and applies predictions by comparing that output to your reference data.
-
-   :::image type="content" source="../media/quickstarts/prediction.png" alt-text="Screenshot of completed schema.":::
-
-6.  Once you're satisfied with the quality of your output, select **Build analyzer**. This action creates an analyzer ID that you can integrate into your own applications, allowing you to call the analyzer from your code.
-
-   :::image type="content" source="../media/quickstarts/build-analyzer.png" alt-text="Screenshot of built analyzer.":::
-
-Now you successfully built your first Content Understanding analyzer, and are ready to start extracting insights from your data. When you select the analyzer you just created, you can view sample code to get started with implenting this in code.
-
-   :::image type="content" source="../media/quickstarts/view-code.png" alt-text="Screenshot of completed schema.":::
-
-Check out [Quickstart: Azure AI Content Understanding REST APIs](./use-rest-api.md) to utilize the REST API to call your analyzer.
-
----
-
 ## Sharing your project
 
 In order to share and manage access to the project you created, navigate to the Management Center, found at the bottom of the navigation for your project:
 
   :::image type="content" source="../media/quickstarts/cu-landing-page.png" alt-text="Screenshot of where to find management center.":::
 
-
 You can manage the users and their individual roles here:
 
    :::image type="content" source="../media/quickstarts/management-center.png" alt-text="Screenshot of Project users section of management center.":::
