more edits

Author: msakande

articles/ai-foundry/foundry-models/how-to/configure-entra-id.md

@@ -5,7 +5,7 @@ description: Learn how to configure key-less authorization to use Azure AI Found
 ms.service: azure-ai-foundry
 ms.subservice: azure-ai-foundry-model-inference
 ms.topic: how-to
-ms.date: 08/29/2025
+ms.date: 09/26/2025
 ms.custom: ignite-2024, github-universe-2024
 author: msakande
 ms.author: mopeakande

articles/ai-foundry/foundry-models/includes/code-create-chat-client-entra.md

@@ -3,20 +3,20 @@ manager: nitinme
 ms.service: azure-ai-foundry
 ms.subservice: azure-ai-foundry-model-inference
 ms.topic: include
-ms.date: 1/21/2025
+ms.date: 09/26/2025
 ms.author: fasantia
 author: santiagxf
 ---
 
 # [Python](#tab/python)
 
-Install the package `azure-ai-inference` using your package manager, like pip:
+Install the `azure-ai-inference` package, using a package manager like pip:
 
 ```bash
 pip install azure-ai-inference
 ```
 
-Then, you can use the package to consume the model. The following example shows how to create a client to consume chat completions with Entra ID:
+Then, use the package to consume the model. The following example shows how to create a client to consume chat completions with Microsoft Entra ID:
 
 ```python
 import os
@@ -32,13 +32,13 @@ client = ChatCompletionsClient(
 
 # [JavaScript](#tab/javascript)
 
-Install the package `@azure-rest/ai-inference` using npm:
+Install the `@azure-rest/ai-inference` package with npm:
 
 ```bash
 npm install @azure-rest/ai-inference
 ```
 
-Then, you can use the package to consume the model. The following example shows how to create a client to consume chat completions with Entra ID:
+Then, use the package to consume the model. The following example shows how to create a client to consume chat completions with Microsoft Entra ID:
 
 ```javascript
 import ModelClient from "@azure-rest/ai-inference";
@@ -76,7 +76,7 @@ using Azure.Identity;
 using Azure.AI.Inference;
 ```
 
-Then, you can use the package to consume the model. The following example shows how to create a client to consume chat completions with Entra ID:
+Then, use the package to consume the model. The following example shows how to create a client to consume chat completions with Microsoft Entra ID:
 
 ```csharp
 TokenCredential credential = new DefaultAzureCredential();
@@ -108,7 +108,7 @@ Add the package to your project:
 </dependency>
 ```
 
-Then, you can use the package to consume the model. The following example shows how to create a client to consume chat completions:
+Then, use the package to consume the model. The following example shows how to create a client to consume chat completions:
 
 ```java
 TokenCredential defaultCredential = new DefaultAzureCredentialBuilder().build();
@@ -118,11 +118,11 @@ ChatCompletionsClient client = new ChatCompletionsClientBuilder()
     .buildClient();
 ```
 
-Explore our [samples](https://github.com/Azure/azure-sdk-for-java/tree/main/sdk/ai/azure-ai-inference/src/samples) and read the [API reference documentation](https://aka.ms/azsdk/azure-ai-inference/java/reference) to get yourself started.
+Explore our [samples](https://github.com/Azure/azure-sdk-for-java/tree/main/sdk/ai/azure-ai-inference/src/samples) and read the [API reference documentation](https://aka.ms/azsdk/azure-ai-inference/java/reference) to get started.
 
 # [REST](#tab/rest)
 
-Use the reference section to explore the API design and which parameters are available and indicate authentication token in the header `Authorization`. For example, the reference section for [Chat completions](../../model-inference/reference/reference-model-inference-chat-completions.md) details how to use the route `/chat/completions` to generate predictions based on chat-formatted instructions. Notice that the path `/models` is included to the root of the URL:
+Use the reference section to explore the API design and see which parameters are available. Indicate the authentication token in the header `Authorization`. For example, the reference section for [Chat completions](../../model-inference/reference/reference-model-inference-chat-completions.md) details how to use the route `/chat/completions` to generate predictions based on chat-formatted instructions. The path `/models` is included in the root of the URL:
 
 __Request__
 
@@ -132,7 +132,7 @@ Authorization: Bearer <bearer-token>
 Content-Type: application/json
 ```
 
-Tokens have to be issued with scope `https://cognitiveservices.azure.com/.default`.
+Tokens must be issued with scope `https://cognitiveservices.azure.com/.default`.
 
 For testing purposes, the easiest way to get a valid token for your user account is to use the Azure CLI. In a console, run the following Azure CLI command:
 

articles/ai-foundry/foundry-models/includes/configure-entra-id/about-credentials.md

@@ -4,15 +4,15 @@ author: santiagxf
 ms.author: fasantia 
 ms.service: azure-ai-foundry
 ms.subservice: azure-ai-foundry-model-inference
-ms.date: 01/23/2025
+ms.date: 09/26/2025
 ms.topic: include
 ---
 
 ### Options for credential when using Microsoft Entra ID
 
-`DefaultAzureCredential` is an opinionated, ordered sequence of mechanisms for authenticating to Microsoft Entra ID. Each authentication mechanism is a class derived from the `TokenCredential` class and is known as a credential. At runtime, `DefaultAzureCredential` attempts to authenticate using the first credential. If that credential fails to acquire an access token, the next credential in the sequence is attempted, and so on, until an access token is successfully obtained. In this way, your app can use different credentials in different environments without writing environment-specific code.
+`DefaultAzureCredential` is an opinionated, ordered sequence of mechanisms for authenticating to Microsoft Entra ID. Each authentication mechanism is a class derived from the `TokenCredential` class and is known as a credential. At runtime, `DefaultAzureCredential` attempts to authenticate by using the first credential. If that credential fails to acquire an access token, the next credential in the sequence is attempted, and so on, until an access token is successfully obtained. In this way, your app can use different credentials in different environments without writing environment-specific code.
 
-When the preceding code runs on your local development workstation, it looks in the environment variables for an application service principal or at locally installed developer tools, such as Visual Studio, for a set of developer credentials. Either approach can be used to authenticate the app to Azure resources during local development.
+When the preceding code runs on your local development workstation, it looks in the environment variables for an application service principal or at locally installed developer tools, such as Visual Studio, for a set of developer credentials. You can use either approach to authenticate the app to Azure resources during local development.
 
 When deployed to Azure, this same code can also authenticate your app to other Azure resources. `DefaultAzureCredential` can retrieve environment settings and managed identity configurations to authenticate to other services automatically.
 

articles/ai-foundry/foundry-models/includes/configure-entra-id/bicep.md

@@ -4,7 +4,7 @@ author: santiagxf
 ms.author: fasantia 
 ms.service: azure-ai-foundry
 ms.subservice: azure-ai-foundry-model-inference
-ms.date: 08/29/2025
+ms.date: 09/26/2025
 ms.topic: include
 zone_pivot_groups: azure-ai-models-deployment
 ---
@@ -19,7 +19,7 @@ zone_pivot_groups: azure-ai-models-deployment
 
 ## About this tutorial
 
-The example in this article is based on code samples contained in the [Azure-Samples/azureai-model-inference-bicep](https://github.com/Azure-Samples/azureai-model-inference-bicep) repository. To run the commands locally without having to copy or paste file content, use the following commands to clone the repository and go to the folder for your coding language:
+The example in this article is based on code samples in the [Azure-Samples/azureai-model-inference-bicep](https://github.com/Azure-Samples/azureai-model-inference-bicep) repository. To run the commands locally without copying or pasting file content, use the following commands to clone the repository and go to the folder for your coding language:
 
 ```azurecli
 git clone https://github.com/Azure-Samples/azureai-model-inference-bicep
@@ -36,12 +36,12 @@ cd azureai-model-inference-bicep/infra
 In this tutorial, you create the following resources:
 
 
-* An Azure AI Foundry (formerly known Azure AI Services) resource with key access disabled. For simplicity, this template doesn't deploy models.
+* An Azure AI Foundry resource (formerly known as Azure AI Services resource) with key access disabled. For simplicity, this template doesn't deploy models.
 * A role-assignment for a given security principal with the role **Cognitive Services User**.
 
 To create these resources, use the following assets:
 
-1. Use the template `modules/ai-services-template.bicep` to describe your Azure AI Foundry (formerly known Azure AI Services) resource:
+1. Use the template `modules/ai-services-template.bicep` to describe your Azure AI Foundry resource:
 
     __modules/ai-services-template.bicep__
 
@@ -107,7 +107,7 @@ After you configure Microsoft Entra ID in your resource, update your code to use
 
 ## Disable key-based authentication in the resource
 
-We advise that you disable key-based authentication when you implement Microsoft Entra ID and fully address compatibility or fallback concerns in all the applications that consume the service. Change the `disableLocalAuth` property to disable key-based authentication:
+Disable key-based authentication when you implement Microsoft Entra ID and fully address compatibility or fallback concerns in all the applications that consume the service. Change the `disableLocalAuth` property to disable key-based authentication:
 
 __modules/ai-services-template.bicep__
 

articles/ai-foundry/foundry-models/includes/configure-entra-id/cli.md

@@ -4,7 +4,7 @@ author: santiagxf
 ms.author: fasantia 
 ms.service: azure-ai-foundry
 ms.subservice: azure-ai-foundry-model-inference
-ms.date: 08/29/2025
+ms.date: 09/26/2025
 ms.topic: include
 zone_pivot_groups: azure-ai-models-deployment
 ---
@@ -17,36 +17,36 @@ zone_pivot_groups: azure-ai-models-deployment
 
   * Your Azure subscription ID.
 
-  * Your Azure AI Foundry (formerly known Azure AI Services) resource name.
+  * Your Azure AI Foundry resource (formerly known as Azure AI Services resource) name.
 
-  * The resource group where you deployed the Azure AI Foundry resource (formerly known Azure AI Services resource).
+  * The resource group where you deployed the Azure AI Foundry resource.
 
 
 ## Configure Microsoft Entra ID for inference
 
 Follow these steps to configure Microsoft Entra ID for inference:
 
 
-1. Sign in to your Azure subscription:
+1. Sign in to your Azure subscription.
 
     ```azurecli
     az login
     ```
 
-1. If you have more than one subscription, select the subscription where your resource is located:
+1. If you have more than one subscription, select the subscription where your resource is located.
 
     ```azurecli
     az account set --subscription "<subscription-id>"
     ```
 
-1. Set the following environment variables with the name of the Azure AI Foundry (formerly known Azure AI Services) resource you plan to use and resource group.
+1. Set the following environment variables with the name of the Azure AI Foundry resource you plan to use and resource group.
 
     ```azurecli
     ACCOUNT_NAME="<ai-services-resource-name>"
     RESOURCE_GROUP="<resource-group>"
     ```
 
-1. Get the full name of your resource:
+1. Get the full name of your resource.
 
     ```azurecli
     RESOURCE_ID=$(az resource show -g $RESOURCE_GROUP -n $ACCOUNT_NAME --resource-type "Microsoft.CognitiveServices/accounts" --query id --output tsv)

articles/ai-foundry/foundry-models/includes/configure-entra-id/intro.md

@@ -4,7 +4,7 @@ author: santiagxf
 ms.author: fasantia 
 ms.service: azure-ai-foundry
 ms.subservice: azure-ai-foundry-model-inference
-ms.date: 08/29/2025
+ms.date: 09/26/2025
 ms.topic: include
 ---
 
@@ -14,17 +14,17 @@ This article explains how to configure Microsoft Entra ID for inference in Azure
 
 ## Understand roles in the context of resource in Azure
 
-Microsoft Entra ID uses the idea of role-based access control (RBAC) for authorization. Roles are central to managing access to your cloud resources. A role is essentially a collection of permissions that define what actions can be performed on specific Azure resources. By assigning roles to users, groups, service principals, or managed identities—collectively known as security principals—you control their access within your Azure environment to specific resources.
+Microsoft Entra ID uses role-based access control (RBAC) for authorization. Roles are central to managing access to your cloud resources. A role is a collection of permissions that define what actions can be performed on specific Azure resources. By assigning roles to users, groups, service principals, or managed identities—collectively known as security principals—you control their access within your Azure environment to specific resources.
 
 When you assign a role, you specify the security principal, the role definition, and the scope. This combination is known as a role assignment. Azure AI Foundry Models is a capability of the Azure AI Services resources, and hence, roles assigned to that particular resource control the access for inference.
 
 You identify two different types of access to the resources:
 
-* **Administration access**: The actions that are related with the administration of the resource. They usually change the state of the resource and its configuration. In Azure, those operations are control-plane operations and can be executed using the Azure portal, the Azure CLI, or with infrastructure as code. Examples of includes creating a new model deployments, changing content filtering configurations, changing the version of the model served, or changing SKU of a deployment.
+* **Administration access**: The actions related to the administration of the resource. They usually change the state of the resource and its configuration. In Azure, those operations are control-plane operations and can be executed using the Azure portal, the Azure CLI, or with infrastructure as code. Examples include creating new model deployments, changing content filtering configurations, changing the version of the model served, or changing SKU of a deployment.
 
-* **Developer access**: The actions that are related with the consumption of the resources. For example, invoking the chat completions API. However, the user can't change the state of the resource and its configuration.
+* **Developer access**: The actions related to the consumption of the resources. For example, invoking the chat completions API. However, the user can't change the state of the resource and its configuration.
 
-In Azure, administration operations are always performed using Microsoft Entra ID. Roles like **Cognitive Services Contributor** allow you to perform those operations. On the other hand, developer operations can be performed using either access keys or/and Microsoft Entra ID. Roles like **Cognitive Services User** allow you to perform those operations.
+In Azure, Microsoft Entra ID always performs administration operations. Roles like **Cognitive Services Contributor** allow you to perform those operations. On the other hand, developer operations can be performed using either access keys or/and Microsoft Entra ID. Roles like **Cognitive Services User** allow you to perform those operations.
 
 > [!IMPORTANT]
 > Having administration access to a resource doesn't necessarily grant developer access to it. Explicit access by granting roles is still required. It's analogous to how database servers work. Having administrator access to the database server doesn't mean you can read the data inside of a database.

articles/ai-foundry/foundry-models/includes/configure-entra-id/portal.md

@@ -15,13 +15,13 @@ zone_pivot_groups: azure-ai-models-deployment
 
 Follow these steps to configure Microsoft Entra ID for inference: 
 
-1. Go to the [Azure portal](https://portal.azure.com) and locate the **Azure AI Foundry (formerly known Azure AI Services)** resource you're using. If you're using Azure AI Foundry with projects or hubs, you can navigate to it by:
+1. Go to the [Azure portal](https://portal.azure.com) and locate the **Azure AI Foundry resource** (formerly known as Azure AI Services resource) you're using. If you're using Azure AI Foundry with projects or hubs, you can navigate to it by:
 
    1. Go to the [Azure AI Foundry portal](https://ai.azure.com/?cid=learnDocs).
 
-   1. On the landing page, select **Open management center**.
+   1. On the landing page, select **Management center**.
 
-   1. Go to the section **Connected resources** and select the connection to the Azure AI Foundry (formerly known Azure AI Services) resource that you want to configure. If it isn't listed, select **View all** to see the full list.
+   1. Go to the section **Connected resources** and select the connection to the Azure AI Foundry resource that you want to configure. If it isn't listed, select **View all** to see the full list.
 
       :::image type="content" source="../../media/configure-entra-id/resource-behind-select.png" alt-text="Screenshot showing how to navigate to the details of the connection in Azure AI Foundry in the management center." lightbox="../../media/configure-entra-id/resource-behind-select.png":::
 
@@ -77,11 +77,11 @@ To change this behavior, update the connections in your projects to use Microsof
 
 1. Go to the [Azure AI Foundry portal](https://ai.azure.com/?cid=learnDocs).
 
-1. Go to the projects or hubs that use the Azure AI Foundry (formerly known Azure AI Services) resource through a connection.
+1. Go to the projects or hubs that use the Azure AI Foundry resource through a connection.
 
 1. Select **Management center**.
 
-1. Go to the **Connected resources** section and select the connection to the Azure AI Foundry (formerly known Azure AI Services) resource that you want to configure. If it's not listed, select **View all** to see the full list.
+1. Go to the **Connected resources** section and select the connection to the Azure AI Foundry resource that you want to configure. If it's not listed, select **View all** to see the full list.
 
 1. In the **Connection details** section, next to **Access details**, select the edit icon.
 
@@ -93,4 +93,4 @@ To change this behavior, update the connections in your projects to use Microsof
 
 ## Disable key-based authentication in the resource
 
-We advise that you disable key-based authentication when you implement Microsoft Entra ID and fully address compatibility or fallback concerns in all the applications that consume the service. You can disable key-based authentication only when deploying with Bicep or ARM.
+Disable key-based authentication when you implement Microsoft Entra ID and fully address compatibility or fallback concerns in all the applications that consume the service. You can disable key-based authentication only when deploying with Bicep or ARM.