acrolinx

Author: lgayhardt

articles/ai-foundry/concepts/evaluation-evaluators/azure-openai-graders.md

@@ -40,7 +40,7 @@ model_config = AzureOpenAIModelConfiguration(
 `AzureOpenAILabelGrader` uses your custom prompt to instruct a model to classify outputs based on labels you define. It returns structured results with explanations for why each label was chosen.
 
 > [!NOTE]
-> We recommend using Azure Open AI GPT o3-mini for best results.
+> We recommend using Azure OpenAI GPT o3-mini for best results.
 
 Here's an example `data.jsonl` that is used in the following code snippets:
 

articles/ai-foundry/how-to/develop/evaluate-sdk.md

@@ -336,11 +336,11 @@ After you spot-check your built-in or custom evaluators on a single row of data,
 
 ### Prerequisite set up steps for Azure AI Foundry Projects
 
-If this is your first time running evaluations and logging it to your Azure AI Foundry project, you may need to do a few additional set up steps.
+If this is your first time running evaluations and logging it to your Azure AI Foundry project, you might need to do a few additional setup steps.
 
 1. [Create and connect your storage account](https://github.com/azure-ai-foundry/foundry-samples/blob/main/samples/microsoft/infrastructure-setup/01-connections/connection-storage-account.bicep) to your Azure AI Foundry project at the resource level. This bicep template provisions and connects a storage account to your Foundry project with key authentication.
 2. Make sure the connected storage account has access to all projects.
-3. If you connected your storage account with Entra ID, make sure to give MSI (Microsoft Identity) permissions for Storage Blob Data Owner to both your account and Foundry project resource in Azure Portal.
+3. If you connected your storage account with Microsoft Entra ID, make sure to give MSI (Microsoft Identity) permissions for Storage Blob Data Owner to both your account and Foundry project resource in Azure portal.
 
 ### Evaluate on a dataset and log results to Azure AI Foundry