@@ -6,7 +6,7 @@ author: PatrickFarley
 ms.author: pafarley
 ms.service: azure-ai-openai
 ms.topic: conceptual
-ms.date: 02/20/2025
+ms.date: 05/31/2025
 manager: nitinme
 ---
 

@@ -6,13 +6,13 @@ author: PatrickFarley
 ms.author: pafarley
 ms.service: azure-ai-openai
 ms.topic: conceptual 
-ms.date: 02/20/2025
+ms.date: 05/31/2025
 manager: nitinme
 ---
 
 # Image prompt engineering techniques
 
-To unlock the full potential of vision-enabled chat models like GPT-4 Turbo with Vision and GPT-4o, it's essential to tailor the prompts to your specific needs. Here are some guidelines to enhance the accuracy and efficiency of your prompts.
+To unlock the full potential of vision-enabled chat models, it's essential to tailor the prompts to your specific needs. Here are some guidelines to enhance the accuracy and efficiency of your prompts.
 
 ## Fundamentals of writing an image prompt
 

@@ -6,13 +6,13 @@ author: PatrickFarley
 ms.author: pafarley
 ms.service: azure-ai-openai
 ms.topic: conceptual 
-ms.date: 02/20/2025
+ms.date: 05/31/2025
 manager: nitinme
 ---
 
 # Vision-enabled chat model concepts
 
-Vision-enabled chat models are large multimodal models (LMM) developed by OpenAI that can analyze images and provide textual responses to questions about them. They incorporate both natural language processing and visual understanding. The current vision-enabled models are GPT-4 Turbo with Vision, GPT-4o, and GPT-4o-mini. This guide provides details on their capabilities and limitations.
+Vision-enabled chat models are large multimodal models (LMM) developed by OpenAI that can analyze images and provide textual responses to questions about them. They incorporate both natural language processing and visual understanding. This guide provides details on their capabilities and limitations. To see which models support image input, see the [Models page](./models.md).
 
 To try out vision-enabled chat models, see the [quickstart](/azure/ai-services/openai/gpt-v-quickstart).
 
@@ -21,6 +21,16 @@ To try out vision-enabled chat models, see the [quickstart](/azure/ai-services/o
 The vision-enabled models answer general questions about what's present in the images you upload.
 
 
+## Input limitations
+
+This section describes the limitations of vision-enabled chat models.
+
+### Image support
+
+- **Maximum input image size**: The maximum size for input images is restricted to 20 MB.
+- **Low resolution accuracy**: When images are analyzed using the "low resolution" setting, it allows for faster responses and uses fewer input tokens for certain use cases. However, this could impact the accuracy of object and text recognition within the image.
+- **Image chat restriction**: When you upload images in [Azure AI Foundry portal](https://ai.azure.com/) or the API, there is a limit of 10 images per chat call.
+
 ## Special pricing information
 
 > [!IMPORTANT]
@@ -70,16 +80,6 @@ For a typical use case, take a 3-minute video with a 100-token prompt input. The
 Additionally, there's a one-time indexing cost of $0.15 to generate the Video Retrieval index for this 3-minute video. This index can be reused across any number of Video Retrieval and GPT-4 Turbo with Vision API calls.
 -->
 
-## Input limitations
-
-This section describes the limitations of vision-enabled chat models.
-
-### Image support
-
-- **Maximum input image size**: The maximum size for input images is restricted to 20 MB.
-- **Low resolution accuracy**: When images are analyzed using the "low resolution" setting, it allows for faster responses and uses fewer input tokens for certain use cases. However, this could impact the accuracy of object and text recognition within the image.
-- **Image chat restriction**: When you upload images in [Azure AI Foundry portal](https://ai.azure.com/) or the API, there is a limit of 10 images per chat call.
-
 <!--
 ### Video support
 

@@ -6,7 +6,7 @@ author: PatrickFarley
 ms.author: pafarley
 ms.service: azure-ai-openai
 ms.topic: conceptual 
-ms.date: 02/20/2025
+ms.date: 05/31/2025
 manager: nitinme
 ---
 

@@ -8,7 +8,7 @@ ms.custom: devx-track-python, devx-track-dotnet, devx-track-extended-java, devx-
 ms.topic: quickstart
 author: PatrickFarley
 ms.author: pafarley
-ms.date: 02/20/2025
+ms.date: 05/31/2025
 zone_pivot_groups: openai-quickstart-dall-e
 ---
 

@@ -9,18 +9,14 @@ ms.custom: devx-track-python, devx-track-js, devx-track-ts
 ms.topic: quickstart
 author: PatrickFarley
 ms.author: pafarley
-ms.date: 02/20/2025
+ms.date: 05/31/2025
 zone_pivot_groups: openai-quickstart-gpt-v
 ---
 
 # Quickstart: Use images in your AI chats
 
-Get started using GPT-4 Turbo with images with the Azure OpenAI in Azure AI Foundry Models.
+Get started using images in your chats with Azure OpenAI in Azure AI Foundry Models.
 
-> [!NOTE]
-> **Model choice**
->
-> The latest vision-capable models are `gpt-4o` and `gpt-4o mini`. These models are in public preview. The latest available GA model is `gpt-4` version `turbo-2024-04-09`.
 
 > [!IMPORTANT]
 > Extra usage fees might apply when using chat completion models with vision functionality.
@@ -62,7 +58,7 @@ Get started using GPT-4 Turbo with images with the Azure OpenAI in Azure AI Foun
 
 ::: zone-end
 
-## Next steps
+## Related content
 
 * [Get started with multimodal vision chat apps using Azure OpenAI AI App template](/azure/developer/ai/get-started-app-chat-vision?tabs=github-codespaces)
 * Learn more about these APIs in the [Vision-enabled models how-to guide](./gpt-v-quickstart.md)

@@ -18,14 +18,39 @@ The evaluation of large language models is a critical step in measuring their pe
 
 Azure OpenAI evaluation enables developers to create evaluation runs to test against expected input/output pairs, assessing the model’s performance across key metrics such as accuracy, reliability, and overall performance.
 
-## Evaluations support
+## Evaluation support
 
 ### Regional availability
 
-- East US2
+- Australia East
+- Brazil South
+- Canada Central
+- Central US
+- East US 2
+- France Central
+- Germany West Central
+- Italy North
+- Japan East
+- Japan West
+- Korea Central
 - North Central US
+- Norway East
+- Poland Central
+- South Africa North
+- Southeast Asia
+- Spain Central
 - Sweden Central
+- Switzerland North
 - Switzerland West
+- UAE North
+- UK South
+- UK West
+- West Europe
+- West US
+- West US 2
+- West US 3
+
+If your preferred region is missing, refer to [Azure OpenAI regions](https://learn.microsoft.com/azure/ai-services/openai/concepts/models?tabs=global-standard%2Cstandard-chat-completions#global-standard-model-availability) and check if it is one of the Azure OpenAI regional availability zones.
 
 ### Supported deployment types
 
@@ -36,6 +61,10 @@ Azure OpenAI evaluation enables developers to create evaluation runs to test aga
 - Global provisioned-managed
 - Data zone provisioned-managed
 
+## Evaluation API (preview)
+
+Evaluation API lets you test model outputs directly through API calls, and programmatically assess model quality and performance. To use Evaluation API, check out the [REST API documentation](https://learn.microsoft.com/azure/ai-services/openai/authoring-reference-preview#evaluation---get-list). 
+
 ## Evaluation pipeline
 
 ### Test data
@@ -90,30 +119,40 @@ Outputs generated during the evaluation will be referenced in subsequent steps u
 
 ### Model deployment
 
-As part of creating evaluations you'll pick which models to use when generating responses (optional) as well as which models to use when grading models with specific testing criteria.  
-
-In Azure OpenAI you'll be assigning specific model deployments to use as part of your evaluations. You can compare multiple model deployments in single evaluation run.
+In Azure OpenAI, you need to create a model deployment to use for your evaluation. You can pick and deploy a single model, or multiple models, depending on your needs. These model deployments will be used when grading your base model or your fine-tuned model with the test criteria of your choice. You can also use the deployed models to auto-generate responses for your provided prompt. 
 
-You can evaluate either base or fine-tuned model deployments. The deployments available in your list depend on those you created within your Azure OpenAI resource. If you can't find the desired deployment, you can create a new one from the Azure OpenAI Evaluation page.
+The deployments available in your list depend on those you created within your Azure OpenAI resource. If you can't find the desired deployment, you can create a new one from the Azure OpenAI Evaluation page.
 
 ### Testing criteria
 
 Testing criteria is used to assess the effectiveness of each output generated by the target model. These tests compare the input data with the output data to ensure consistency. You have the flexibility to configure different criteria to test and measure the quality and relevance of the output at different levels.
 
-:::image type="content" source="../media/how-to/evaluations/testing-criteria.png" alt-text="Screenshot that shows the evaluations testing criteria options." lightbox="../media/how-to/evaluations/testing-criteria.png":::
+:::image type="content" source="../media/how-to/evaluations/eval-testing-criteria.png" alt-text="Screenshot that shows the evaluations testing criteria options." lightbox="../media/how-to/evaluations/eval-testing-criteria.png":::
+
+When you click into each testing criteria, you will see different types of graders as well as preset schemas that you can modify per your own evaluation dataset and criteria. 
+
+:::image type="content" source="../media/how-to/evaluations/eval-testing-criteria-2.png" alt-text="Screenshot that shows the evaluations testing criteria options." lightbox="../media/how-to/evaluations/eval-testing-criteria-2.png":::
 
 ## Getting started
 
 1. Select the **Azure OpenAI Evaluation (PREVIEW)** within [Azure AI Foundry portal](https://ai.azure.com/). To see this view as an option may need to first select an existing Azure OpenAI resource in a supported region.
-2. Select **New evaluation**
+2. Select **+ New evaluation**
 
     :::image type="content" source="../media/how-to/evaluations/new-evaluation.png" alt-text="Screenshot of the Azure OpenAI evaluation UX with new evaluation selected." lightbox="../media/how-to/evaluations/new-evaluation.png":::
 
-3. Enter a name of your evaluation. By default a random name is automatically generated unless you edit and replace it. Select **Upload new dataset**.
+3. Choose how you would like to provide test data for evaluation. You can import stored Chat Completions, create data using provided default templates, or upload your own data. Let's walk through uploading your own data. 
+
+    :::image type="content" source="../media/how-to/evaluations/create-new-eval.png" alt-text="Screenshot of the Azure OpenAI create new evaluation." lightbox="../media/how-to/evaluations/create-new-eval.png":::
 
-    :::image type="content" source="../media/how-to/evaluations/upload.png" alt-text="Screenshot of the Azure OpenAI upload UX." lightbox="../media/how-to/evaluations/upload.png":::
+4. Select your evaluation data which will be in `.jsonl` format. If you already have an existing data, you can select one, or upload a new data.
 
-4. Select your evaluation which will be in `.jsonl` format. If you need a sample test file you can save these 10 lines to a file called `eval-test.jsonl`:
+    :::image type="content" source="../media/how-to/evaluations/upload-data-1.png" alt-text="Screenshot of data upload." lightbox="../media/how-to/evaluations/upload-data-1.png":::
+
+   When you upload new data, you'll see the first three lines of the file as a preview on the right side:
+
+    :::image type="content" source="../media/how-to/evaluations/upload-data-2.png" alt-text="Screenshot of data upload." lightbox="../media/how-to/evaluations/upload-data-2.png":::
+
+   If you need a sample test file, you can use this sample `.jsonl` text. This sample contains sentences of various technical content, and we are going to be assessing semantic similarity across these sentences.
 
     ```jsonl
     {"input": [{"role": "system", "content": "Provide a clear and concise summary of the technical content, highlighting key concepts and their relationships. Focus on the main ideas and practical implications."}, {"role": "user", "content": "Tokenization is a key step in preprocessing for natural language processing, involving the division of text into smaller components called tokens. These can be words, subwords, or characters, depending on the method chosen. Word tokenization divides text at word boundaries, while subword techniques like Byte Pair Encoding (BPE) or WordPiece can manage unknown words by breaking them into subunits. Character tokenization splits text into individual characters, useful for multiple languages and misspellings. The tokenization method chosen greatly affects model performance and its capacity to handle various languages and vocabularies."}], "output": "Tokenization divides text into smaller units (tokens) for NLP applications, using word, subword (e.g., BPE), or character methods. Each has unique benefits, impacting model performance and language processing capabilities."}      
@@ -127,48 +166,45 @@ Testing criteria is used to assess the effectiveness of each output generated by
     {"input": [{"role": "system", "content": "Generate a clear and focused summary that captures the essential technical details while maintaining their relationships and significance."}, {"role": "user", "content": "Transfer learning is a machine learning method where a model developed for one task is reused as the starting point for a model on a second task. This approach is powerful in deep learning, where pre-trained models on large datasets (like ImageNet for computer vision or BERT for NLP) are fine-tuned on specific downstream tasks. Transfer learning reduces the need for large amounts of task-specific training data and computational resources, as the model has already learned useful features from the source domain. Common strategies include feature extraction (freezing pre-trained layers) and fine-tuning (updating all or some pre-trained weights)."}], "output": "Transfer learning reuses models trained on one task for different tasks, particularly effective in deep learning. It leverages pre-trained models through feature extraction or fine-tuning, reducing data and computational needs for new tasks."}      
     {"input": [{"role": "system", "content": "Provide a precise and informative summary that distills the key technical concepts while maintaining their relationships and practical importance."}, {"role": "user", "content": "Ensemble methods combine multiple machine learning models to create a more robust and accurate predictor. Common techniques include bagging (training models on random data subsets), boosting (sequentially training models to correct earlier errors), and stacking (using a meta-model to combine base model predictions). Random Forests, a popular bagging method, create multiple decision trees using random feature subsets. Gradient Boosting builds trees sequentially, with each tree correcting the errors of previous ones. These methods often outperform single models by reducing overfitting and variance while capturing different data aspects."}], "output": "Ensemble methods enhance prediction accuracy by combining multiple models through techniques like bagging, boosting, and stacking. Popular implementations include Random Forests (using multiple trees with random features) and Gradient Boosting (sequential error correction), offering better performance than single models."}
     ```
+    
+5. If you would like to create new responses using inputs from your test data, you can select 'Generate new responses'. This will inject the input fields from our evaluation file into individual prompts for a model of your choice to generate output.
 
-    You'll see the first three lines of the file as a preview:
-
-    :::image type="content" source="../media/how-to/evaluations/preview.png" alt-text="Screenshot that shows a preview of an uploaded evaluation file." lightbox="../media/how-to/evaluations/preview.png":::
-
-5. Under **Responses** select the **Create** button. Select `{{item.input}}` from the **Create with a template** dropdown. This will inject the input fields from our evaluation file into individual prompts for a new model run that we want to able to compare against our evaluation dataset. The model will take that input and generate its own unique outputs which in this case will be stored in a variable called `{{sample.output_text}}`. We'll then use that sample output text later as part of our testing criteria. Alternatively you could provide your own custom system message and individual message examples manually.
-
-6. Select which model you want to generate responses based on your evaluation. If you don't have a model you can create one. For the purpose of this example we're using a standard deployment of `gpt-4o-mini`.
+:::image type="content" source="../media/how-to/evaluations/eval-generate-1.png" alt-text="Screenshot of the UX for generating model responses." lightbox="../media/how-to/evaluations/eval-generate-1.png":::
+   
+You will select the model of your choice. If you do not have a model, you can create a new model deployment. The selected model will take the input data and generate its own unique outputs, which in this case will be stored in a variable called `{{sample.output_text}}`. We'll then use that output later as part of our testing criteria. Alternatively, you could provide your own custom system message and individual message examples manually.
 
-    :::image type="content" source="../media/how-to/evaluations/item-input.png" alt-text="Screenshot of the UX for generating model responses with a model selected." lightbox="../media/how-to/evaluations/item-input.png":::
+:::image type="content" source="../media/how-to/evaluations/eval-generate-2.png" alt-text="Screenshot of the UX for generating model responses." lightbox="../media/how-to/evaluations/eval-generate-2.png":::
 
-    The settings/sprocket symbol controls the basic parameters that are passed to the model. Only the following parameters are supported at this time:
+6. For creating a test criteria, select **Add**. For the example file we provided, we are going to be assessing semantic similarity. Select **Model Scorer**, which contains test criteria presets for Semantic Similarity.
 
-- **Temperature**
-- **Maximum length**
-- **Top P**
+    :::image type="content" source="../media/how-to/evaluations/eval-semantic-similarity-1.png" alt-text="Screenshot of the semantic similarity UX config." lightbox="../media/how-to/evaluations/eval-semantic-similarity-1.png":::
 
- Maximum length is currently capped at 2048 regardless of what model you select.
+   Select **Semantic Similarity** at the top. Scroll to the bottom, and in `User` section, specify `{{item.output}}` as `Ground truth`, and specify `{{sample.output_text}}` as `Output`. This will take the original reference output from your evaluation `.jsonl` file (the sample file provided) and compare it against the output that is generated by the model you chose in the previous step.
 
-7. Select **Add testing criteria** select **Add**.
+    :::image type="content" source="../media/how-to/evaluations/eval-semantic-similarity-2.png" alt-text="Screenshot of the semantic similarity UX config." lightbox="../media/how-to/evaluations/eval-semantic-similarity-2.png":::
 
-8. Select **Semantic Similarity** >  Under **Compare** add `{{item.output}}` under **With** add ``{{sample.output_text}}``. This will take the original reference output from your evaluation `.jsonl` file and compare it against the output that will be generated by giving the model prompts based on your ``{{item.input}}``.
+:::image type="content" source="../media/how-to/evaluations/eval-semantic-similarity-3.png" alt-text="Screenshot of the semantic similarity UX config." lightbox="../media/how-to/evaluations/eval-semantic-similarity-3.png":::
 
-    :::image type="content" source="../media/how-to/evaluations/semantic-similarity-config.png" alt-text="Screenshot of the semantic similarity UX config." lightbox="../media/how-to/evaluations/semantic-similarity-config.png":::
+7. Select **Add** to add this testing criteria. If you would like to add additional testing criteria, you can add them at this step.
 
-9. Select **Add** > at this point you can either add additional testing criteria or you select Create to initiate the evaluation job run.
+8. You are ready to create your Evaluation. Provide your Evaluation name, review everything looks correct, and **Submit** to create the Evaluation job. You'll be taken to a status page for your evaluation job, which will show the status as "Waiting".
 
-10. Once you select **Create** you'll be taken to a status page for your evaluation job.
+:::image type="content" source="../media/how-to/evaluations/eval-submit-job.png" alt-text="Screenshot of the evaluation job submit UX." lightbox="../media/how-to/evaluations/eval-submit-job.png":::
+:::image type="content" source="../media/how-to/evaluations/eval-submit-job-2.png" alt-text="Screenshot of the evaluation job submit UX." lightbox="../media/how-to/evaluations/eval-submit-job-2.png":::
 
-    :::image type="content" source="../media/how-to/evaluations/status.png" alt-text="Screenshot of the evaluation status UX." lightbox="../media/how-to/evaluations/status.png":::
+9. Once your evaluation job has created, you can select the job to view the full details of the job:
 
-11. Once your evaluation job has created you can select the job to view the full details of the job:
+:::image type="content" source="../media/how-to/evaluations/test-complete.png" alt-text="Screenshot of a completed semantic similarity test with mix of pass and failures." lightbox="../media/how-to/evaluations/test-complete.png":::
 
-    :::image type="content" source="../media/how-to/evaluations/test-complete.png" alt-text="Screenshot of a completed semantic similarity test with mix of pass and failures." lightbox="../media/how-to/evaluations/test-complete.png":::
+10. For semantic similarity **View output details** contains a JSON representation that you can copy/paste of your passing tests.
 
-12. For semantic similarity **View output details** contains a JSON representation that you can copy/paste of your passing tests.
+:::image type="content" source="../media/how-to/evaluations/output-details.png" alt-text="Screenshot of the evaluation status UX with output details." lightbox="../media/how-to/evaluations/output-details.png":::
 
-    :::image type="content" source="../media/how-to/evaluations/output-details.png" alt-text="Screenshot of the evaluation status UX with output details." lightbox="../media/how-to/evaluations/output-details.png":::
+11. You can also add more Eval runs by selecting **+ Add Run** button at the top left corner of your evaluation job page.
 
-## Testing criteria details
+## Types of Testing Criteria
 
-Azure OpenAI Evaluation offers multiple testing criteria options. The section below provides additional details on each option.
+Azure OpenAI Evaluation offers various evaluation testing criteria on top of Semantic Similarity we saw in the provided example. This section provides information about each testing criteria at much more detail.
 
 ### Factuality
 

@@ -7,14 +7,14 @@ ms.author: pafarley #delegenz
 #customer intent: As a developer, I want to learn how to use vision-enabled chat models so that I can integrate image processing capabilities into my applications.
 ms.service: azure-ai-openai
 ms.topic: how-to
-ms.date: 02/20/2025
+ms.date: 05/31/2025
 manager: nitinme
 ---
 
 # Use vision-enabled chat models
 
 
-Vision-enabled chat models are large multimodal models (LMM) developed by OpenAI that can analyze images and provide textual responses to questions about them. They incorporate both natural language processing and visual understanding. The current vision-enabled models are [o1](./reasoning.md), GPT-4o, GPT-4o-mini, and GPT-4 Turbo with Vision.
+Vision-enabled chat models are large multimodal models (LMM) developed by OpenAI that can analyze images and provide textual responses to questions about them. They incorporate both natural language processing and visual understanding. The current vision-enabled models are the [o-series reasoning models](./reasoning.md), GPT-4.1 series models, GPT-4.5, GPT-4o series, and GPT-4 Turbo with Vision.
 
 The vision-enabled models can answer general questions about what's present in the images you upload.
 
@@ -383,7 +383,7 @@ Every response includes a `"finish_reason"` field. It has the following possible
 [!INCLUDE [GPT-4 Turbo](../includes/gpt-4-turbo.md)]
 
 
-## Next steps
+## Related content
 
 * [Learn more about Azure OpenAI](../overview.md).
 * [Vision-enabled chats quickstart](../gpt-v-quickstart.md)

@@ -6,7 +6,7 @@ author: PatrickFarley
 ms.author: pafarley 
 ms.service: azure-ai-openai
 ms.topic: how-to
-ms.date: 02/20/2025
+ms.date: 05/31/2025
 manager: nitinme
 ---
 
@@ -43,14 +43,14 @@ Adjust your content filter configuration to further align with business needs an
 
 ## Potentially abusive user detection   
 
-The **Potentially abusive user detection** pane leverages user-level abuse reporting to show information about users whose behavior has resulted in blocked content. The goal is to help you get a view of the sources of harmful content so you can take responsive actions to ensure the model is being used in a responsible way. 
+The **Potentially abusive user detection** pane shows information about users whose behavior has resulted in blocked content. The goal is to help you get a view of the sources of harmful content so you can take responsive actions to ensure the model is being used in a responsible way. 
 
 
 To use Potentially abusive user detection, you need:
 - A content filter configuration applied to your deployment.
 - You must be sending user ID information in your Chat Completion requests (see the _user_ parameter of the [Completions API](/azure/ai-services/openai/reference#completions), for example).
     > [!CAUTION]
-    > Use GUID strings to identify individual users. Do not include sensitive personal information in the _user_ field.
+    > Use GUID strings to identify individual users. Don't include sensitive personal information in the _user_ field.
 - An Azure Data Explorer database set up to store the user analysis results (instructions below).
 
 ### Set up your Azure Data Explorer database

@@ -5,7 +5,7 @@ description: Learn how to use blocklists with Azure OpenAI
 manager: nitinme
 ms.service: azure-ai-openai
 ms.topic: how-to
-ms.date: 02/20/2025
+ms.date: 05/31/2025
 author: PatrickFarley
 ms.author: pafarley
 ---
@@ -17,7 +17,7 @@ The [configurable content filters](/azure/ai-services/openai/how-to/content-filt
 ## Prerequisites
 
 - An Azure subscription. <a href="https://azure.microsoft.com/free/ai-services" target="_blank">Create one for free</a>.
-- Once you have your Azure subscription, create an Azure OpenAI resource in the Azure portal to get your token, key and endpoint. Enter a unique name for your resource, select the subscription you entered on the application form, select a resource group, supported region, and supported pricing tier. Then select **Create**.
+- Once you have your Azure subscription, create an Azure OpenAI resource in the Azure portal to get your token, key, and endpoint. Enter a unique name for your resource, select the subscription you entered on the application form, select a resource group, supported region, and supported pricing tier. Then select **Create**.
     - The resource takes a few minutes to deploy. After it finishes, select **go to resource**. In the left pane, under **Resource Management**, select **Subscription Key and Endpoint**. The endpoint and either of the keys are used to call APIs.
 - [Azure CLI](/cli/azure/install-azure-cli) installed
 - [cURL](https://curl.haxx.se/) installed
@@ -30,7 +30,7 @@ You can create blocklists with the Azure OpenAI API. The following steps help yo
 
 ### Get your token
 
-First, you need to get a token for accessing the APIs for creating, editing and deleting blocklists. You can get this token using the following Azure CLI command: 
+First, you need to get a token for accessing the APIs for creating, editing, and deleting blocklists. You can get this token using the following Azure CLI command: 
 
 ```bash
 az account get-access-token 
@@ -102,7 +102,7 @@ Copy the cURL command below to a text editor and make the following changes:
 1. Replace {raiBlocklistName} (in the URL) with a custom name for your list. Allowed characters: `0-9, A-Z, a-z, - . _ ~`. 
 1. Replace {raiBlocklistItemName} with a custom name for your list item. 
 1. Replace {token} with the token you got from the "Get your token" step above. 
-1. Replace the value of the `"blocking pattern"` field with the item you'd like to add to your blocklist. The maximum length of a blockItem is 1000 characters. Also specify whether the pattern is regex or exact match. 
+1. Replace the value of the `"blocking pattern"` field with the item you'd like to add to your blocklist. The maximum length of a blockItem is 1,000 characters. Also specify whether the pattern is regex or exact match. 
 
 ```bash
 curl --location --request PUT 'https://management.azure.com/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.CognitiveServices/accounts/{accountName}/raiBlocklists/{raiBlocklistName}/raiBlocklistItems/{raiBlocklistItemName}?api-version=2024-04-01-preview' \ 
@@ -117,7 +117,7 @@ curl --location --request PUT 'https://management.azure.com/subscriptions/{subsc
 ```
 
 > [!NOTE]
-> It can take around 5 minutes for a new term to be added to the blocklist. Please test after 5 minutes. 
+> It can take around 5 minutes for a new term to be added to the blocklist. Test the blocklist after 5 minutes. 
 
 The response code should be `200`.