Merge pull request #6905 from PatrickFarley/freshness-pass

Freshness pass

Author: JamesJBarnett

articles/ai-foundry/openai/how-to/dall-e.md

@@ -1,11 +1,11 @@
 ---
-title: How to use image generation models
+title: How to Use Image Generation Models from OpenAI
 titleSuffix: Azure OpenAI in Azure AI Foundry Models
-description: Learn how to generate and edit images with image models, and learn about the configuration options that are available.
+description: Learn how to generate and edit images using Azure OpenAI image generation models. Discover configuration options and start creating images today.
 author: PatrickFarley
 ms.author: pafarley
 manager: nitinme
-ms.date: 04/23/2025
+ms.date: 09/02/2025
 ms.service: azure-ai-openai
 ms.topic: how-to
 ms.custom:
@@ -15,22 +15,26 @@ ms.custom:
 
 # How to use Azure OpenAI image generation models
 
-OpenAI's image generation models render images based on user-provided text prompts and optionally provided images. This guide demonstrates how to use the image generation models and configure their options through REST API calls.
+OpenAI's image generation models create images from user-provided text prompts and optional images. This article explains how to use these models, configure options, and benefit from advanced image generation capabilities in Azure.
 
 
 ## Prerequisites
 
+
 - An Azure subscription. You can [create one for free](https://azure.microsoft.com/pricing/purchase-options/azure-account?icid=ai-services).
 - An Azure OpenAI resource created in a supported region. See [Region availability](/azure/ai-foundry/openai/concepts/models#model-summary-table-and-region-availability).
 - Deploy a `dall-e-3` or `gpt-image-1` model with your Azure OpenAI resource. For more information on deployments, see [Create a resource and deploy a model with Azure OpenAI](/azure/ai-foundry/openai/how-to/create-resource).
     - GPT-image-1 is the newer model and features a number of improvements over DALL-E 3. It's available in limited access: apply for access with [this form](https://aka.ms/oai/gptimage1access).
 
-## Call the Image Generation API
 
-The following command shows the most basic way to use an image model with code. If this is your first time using these models programmatically, we recommend starting with the [quickstart](/azure/ai-foundry/openai/dall-e-quickstart).
+## Call the image generation API
+
+
+The following command shows the most basic way to use an image model with code. If this is your first time using these models programmatically, start with the [quickstart](/azure/ai-foundry/openai/dall-e-quickstart).
 
 
 #### [GPT-image-1](#tab/gpt-image-1)
+
 Send a POST request to:
 
 ```
@@ -41,14 +45,18 @@ https://<your_resource_name>.openai.azure.com/openai/deployments/<your_deploymen
 **URL**:
 
 Replace the following values:
+
 - `<your_resource_name>` is the name of your Azure OpenAI resource.
 - `<your_deployment_name>` is the name of your DALL-E 3 or GPT-image-1 model deployment.
 - `<api_version>` is the version of the API you want to use. For example, `2025-04-01-preview`.
 
+
 **Required headers**:
+
 - `Content-Type`: `application/json`
 - `api-key`: `<your_API_key>`
 
+
 **Body**:
 
 The following is a sample request body. You specify a number of options, defined in later sections.
@@ -122,7 +130,7 @@ The response from a successful image generation API call looks like the followin
 } 
 ```
 > [!NOTE]
-> `response_format` parameter is not supported for GPT-image-1 which always returns base64-encoded images.
+> The `response_format` parameter isn't supported for GPT-image-1, which always returns base64-encoded images.
 
 #### [DALL-E 3](#tab/dalle-3)
 
@@ -144,7 +152,7 @@ The response from a successful image generation API call looks like the followin
 
 ### API call rejection
 
-Prompts and images are filtered based on our content policy, returning an error when a prompt or image is flagged.
+Prompts and images are filtered based on our content policy. The API returns an error when a prompt or image is flagged.
 
 If your prompt is flagged, the `error.code` value in the message is set to `contentFilter`. Here's an example:
 
@@ -172,9 +180,9 @@ It's also possible that the generated image itself is filtered. In this case, th
 }
 ```
 
-### Write text-to-image prompts
+### Write effective text-to-image prompts
 
-Your prompts should describe the content you want to see in the image, and the visual style of image.
+Your prompts should describe the content you want to see in the image and the visual style of the image.
 
 When you write prompts, consider that the Image APIs come with a content moderation filter. If the service recognizes your prompt as harmful content, it doesn't generate an image. For more information, see [Content filtering](../concepts/content-filter.md).
 
@@ -197,7 +205,7 @@ Specify the size of the generated images. Must be one of `1024x1024`, `1024x1536
 
 #### Quality
 
-There are three options for image quality: `low`, `medium`, and `high`.Lower quality images can be generated faster.
+There are three options for image quality: `low`, `medium`, and `high`. Lower quality images can be generated faster.
 
 The default value is `high`.
 
@@ -207,22 +215,22 @@ You can generate between one and 10 images in a single API call. The default val
 
 #### User ID
 
-Use the *user* parameter to specify a unique identifier for the user making the request. This is useful for tracking and monitoring usage patterns. The value can be any string, such as a user ID or email address.
+Use the *user* parameter to specify a unique identifier for the user making the request. This identifier is useful for tracking and monitoring usage patterns. The value can be any string, such as a user ID or email address.
 
 #### Output format
 
 Use the *output_format* parameter to specify the format of the generated image. Supported formats are `PNG` and `JPEG`. The default is `PNG`.
 
 > [!NOTE]
-> WEBP images are not supported in the Azure OpenAI in Azure AI Foundry Models.
+> WEBP images aren't supported in the Azure OpenAI in Azure AI Foundry Models.
 
 #### Compression
 
 Use the *output_compression* parameter to specify the compression level for the generated image. Input an integer between `0` and `100`, where `0` is no compression and `100` is maximum compression. The default is `100`.
 
 #### Streaming 
 
-Use the *stream* parameter to enable streaming responses. When set to `true`, the API returns partial images as they are generated. This provides faster visual feedback for users and improves perceived latency. Set the *partial_images* parameter to control how many partial images are generated (1-3).
+Use the *stream* parameter to enable streaming responses. When set to `true`, the API returns partial images as they're generated. This feature provides faster visual feedback for users and improves perceived latency. Set the *partial_images* parameter to control how many partial images are generated (1-3).
 
 
 #### [DALL-E 3](#tab/dalle-3)
@@ -251,23 +259,23 @@ The default value is `vivid`.
 
 #### Quality
 
-There are two options for image quality: `hd` and `standard`. The hd option creates images with finer details and greater consistency across the image. Standard images can be generated faster.
+There are two options for image quality: `hd` and `standard`. The hd option creates images with finer details and greater consistency across the image. Standard images are faster to generate.
 
 The default value is `standard`.
 
 #### Number
 
-With DALL-E 3, you can't generate more than one image in a single API call: the `n` parameter must be set to *1*. If you need to generate multiple images at once, make parallel requests.
+With DALL-E 3, you can't generate more than one image in a single API call: the `n` parameter must be set to *1*. To generate multiple images at once, make parallel requests.
 
 #### Response format
 
-The format in which DALL-E 3 generated images are returned. Must be one of `url` or `b64_json`. This parameter isn't supported for GPT-image-1 which always returns base64-encoded images.
+The format in which DALL-E 3 returns generated images. Must be one of `url` or `b64_json`. This parameter isn't supported for GPT-image-1, which always returns base64-encoded images.
 
 ---
 
-## Call the Image Edit API
+## Call the image edit API
 
-The Image Edit API allows you to modify existing images based on text prompts you provide. The API call is similar to the image generation API call, but you also need to provide an input image.
+The Image Edit API enables you to modify existing images based on text prompts you provide. The API call is similar to the image generation API call, but you also need to provide an input image.
 
 
 #### [GPT-image-1](#tab/gpt-image-1)
@@ -308,8 +316,7 @@ The following is a sample request body. You specify a number of options, defined
 -F "n=1" \
 -F "quality=high"
 ```
-
-### Output
+### API response output
 
 The response from a successful image editing API call looks like the following example. The `b64_json` field contains the output image data.
 
@@ -324,28 +331,28 @@ The response from a successful image editing API call looks like the following e
 } 
 ```
 
-### Specify API options
+### Specify image edit API options
 
 The following API body parameters are available for image editing models, in addition to the ones available for image generation models.
 
-### Image
+#### Image
 
 The *image* value indicates the image file you want to edit.
 
 #### Input fidelity 
 
-The *input_fidelity* parameter controls how much effort the model will exert to match the style and features, especially facial features, of input images 
+The *input_fidelity* parameter controls how much effort the model puts into matching the style and features, especially facial features, of input images. 
 
-This allows you to make subtle edits to an image without altering unrelated areas. When you use high input fidelity, faces are preserved more accurately than in standard mode. 
+This parameter lets you make subtle edits to an image without changing unrelated areas. When you use high input fidelity, faces are preserved more accurately than in standard mode. 
 
 
 #### Mask
 
-The *mask* parameter is the same type as the main *image* input parameter. It defines the area of the image that you want the model to edit, using fully transparent pixels (alpha of zero) in those areas. The mask must be a PNG file and have the same dimensions as the input image.
+The *mask* parameter uses the same type as the main *image* input parameter. It defines the area of the image that you want the model to edit, using fully transparent pixels (alpha of zero) in those areas. The mask must be a PNG file and have the same dimensions as the input image.
 
 #### Streaming 
 
-Use the *stream* parameter to enable streaming responses. When set to `true`, the API returns partial images as they are generated. This provides faster visual feedback for users and improves perceived latency. Set the *partial_images* parameter to control how many partial images are generated (1-3).
+Use the *stream* parameter to enable streaming responses. When set to `true`, the API returns partial images as they're generated. This feature provides faster visual feedback for users and improves perceived latency. Set the *partial_images* parameter to control how many partial images are generated (1-3).
 
 #### [DALL-E 3](#tab/dalle-3)
 

articles/ai-foundry/openai/how-to/model-router.md

@@ -4,7 +4,7 @@ description: Learn how to use the model router in Azure OpenAI to select the bes
 author: PatrickFarley
 ms.author: pafarley
 manager: nitinme
-ms.date: 08/12/2025
+ms.date: 09/02/2025
 ms.service: azure-ai-openai
 ms.topic: how-to
 ms.custom:
@@ -14,9 +14,9 @@ ms.custom:
 
 # Use model router for Azure AI Foundry (preview)
 
-Model router for Azure AI Foundry is a deployable AI chat model that is trained to select the best large language model (LLM) to respond to a given prompt in real time. It uses a combination of preexisting models to provide high performance while saving on compute costs where possible, all packaged as a single model deployment. For more information on how model router works and its advantages and limitations, see the [Model router concepts guide](../concepts/model-router.md).
+Model router for Azure AI Foundry is a deployable AI chat model that selects the best large language model (LLM) to respond to a prompt in real time. It uses different preexisting models to deliver high performance and save compute costs, all in one model deployment. To learn more about how model router works, its advantages, and limitations, see the [Model router concepts guide](../concepts/model-router.md).
 
-You can access model router through the Completions API just as you would use a single base model like GPT-4. The steps are the same as in the [Chat completions guide](/azure/ai-foundry/openai/how-to/chatgpt).
+Use model router through the Completions API like you use a single base model such as GPT-4. Follow the same steps as in the [Chat completions guide](/azure/ai-foundry/openai/how-to/chatgpt).
 
 ## Deploy a model router model
 
@@ -26,17 +26,14 @@ You can access model router through the Completions API just as you would use a
 Model router is packaged as a single Azure AI Foundry model that you deploy. Follow the steps in the [resource deployment guide](/azure/ai-foundry/openai/how-to/create-resource). In the **Create new deployment** step, find `model-router` in the **Models** list. Select it, and then complete the rest of the deployment steps.
 
 > [!NOTE]
-> Consider that your deployment settings apply to all underlying chat models that model router uses.
-> - You don't need to deploy the underlying chat models separately. Model router works independently of your other deployed models.
-> - You select a content filter when you deploy the model router model (or you can apply a filter later). The content filter is applied to all content passed to and from the model router: you don't set content filters for each of the underlying chat models.
-> - Your tokens-per-minute rate limit setting is applied to all activity to and from the model router: you don't set rate limits for each of the underlying chat models.
-
-## Use model router in chats
+> Your deployment settings apply to all underlying chat models that model router uses.
+> - Don't deploy the underlying chat models separately. Model router works independently of your other deployed models.
+> - Select a content filter when you deploy the model router model or apply a filter later. The content filter applies to all content passed to and from the model router; don't set content filters for each underlying chat model.
+> - Your tokens-per-minute rate limit setting applies to all activity to and from the model router; don't set rate limits for each underlying chat model.## Use model router in chats
 
 You can use model router through the [chat completions API](/azure/ai-foundry/openai/chatgpt-quickstart) in the same way you'd use other OpenAI chat models. Set the `model` parameter to the name of our model router deployment, and set the `messages` parameter to the messages you want to send to the model.
 
-In the [Azure AI Foundry portal](https://ai.azure.com/?cid=learnDocs), you can navigate to your model router deployment on the **Models + endpoints** page and select it to enter the model playground. In the playground experience, you can enter messages and see the model's responses. Each response message shows which underlying model was selected to respond.
-
+In the [Azure AI Foundry portal](https://ai.azure.com/?cid=learnDocs), go to your model router deployment on the **Models + endpoints** page and select it to open the model playground. In the playground, enter messages and see the model's responses. Each response shows which underlying model the router selected.
 
 > [!IMPORTANT]
 > You can set the `Temperature` and `Top_P` parameters to the values you prefer (see the [concepts guide](/azure/ai-foundry/openai/concepts/prompt-engineering?tabs=chat#temperature-and-top_p-parameters)), but note that reasoning models (o-series) don't support these parameters. If model router selects a reasoning model for your prompt, it ignores the `Temperature` and `Top_P` input parameters.
@@ -145,12 +142,11 @@ The JSON response you receive from a model router model is identical to the stan
 
 ### Monitor performance
 
-You can monitor the performance of your model router deployment in Azure monitor (AzMon) in the Azure portal. 
+Monitor the performance of your model router deployment in Azure Monitor (AzMon) in the Azure portal.
 
-1. Go to the **Monitoring** -> **Metrics** page for your Azure OpenAI resource in the Azure portal. 
+1. Go to the **Monitoring** > **Metrics** page for your Azure OpenAI resource in the Azure portal.
 1. Filter by the deployment name of your model router model.
-1. Optionally, split up the metrics by underlying models.
-
+1. Split the metrics by underlying models if needed.
 
 ### Monitor costs
 

articles/ai-services/computer-vision/concept-brand-detection.md

@@ -7,7 +7,7 @@ manager: nitinme
 
 ms.service: azure-ai-vision
 ms.topic: conceptual
-ms.date: 01/22/2025
+ms.date: 09/02/2025
 ms.author: pafarley
 ---
 

articles/ai-services/computer-vision/how-to/identity-detect-faces.md

@@ -10,7 +10,7 @@ ms.service: azure-ai-vision
 ms.subservice: azure-ai-face
 ms.update-cycle: 90-days
 ms.topic: how-to
-ms.date: 08/21/2025
+ms.date: 09/02/2025
 ms.author: pafarley
 ms.devlang: csharp
 ms.custom: devx-track-csharp