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Fixed typos flagged by Acrolinx and added period at the end of alt text.

Author: PMEds28

learn-pr/wwl-data-ai/evaluate-models-azure-ai-studio/3b-automated-evaluations.yml

@@ -3,7 +3,7 @@ uid: learn.wwl.evaluate-models-azure-ai-studio.automated-evaluations
 title: Automated evaluations
 metadata:
   title: Automated evaluations
-  description: "Learn how use autmated evaluations in the Azure AI Foundry portal."
+  description: "Learn how use automated evaluations in the Azure AI Foundry portal."
   ms.date: 04/16/2025
   author: madiepev
   ms.author: madiepev

learn-pr/wwl-data-ai/evaluate-models-azure-ai-studio/6-knowledge-check.yml

@@ -34,7 +34,7 @@ quiz:
     - content: "Risk and safety"
       isCorrect: false
       explanation: "Incorrect. "
-  - content: "YYou want to evaluate the grammatical and linguistic quality of responses. What kind of metrics should you specify for automated evaluations?"
+  - content: "You want to evaluate the grammatical and linguistic quality of responses. What kind of metrics should you specify for automated evaluations?"
     choices:
     - content: "AI quality (AI-assisted)"
       isCorrect: true

learn-pr/wwl-data-ai/evaluate-models-azure-ai-studio/includes/3b-automated-evaluations.md

@@ -4,7 +4,7 @@ Automated evaluations in Azure AI Foundry portal enable you to assess the qualit
 
 To evaluate a model, you need a dataset of prompts and responses (and optionally, expected responses as "ground truth"). You can compile this dataset manually or use the output from an existing application; but a useful way to get started is to use an AI model to generate a set of prompts and responses related to a specific subject. You can then edit the generated prompts and responses to reflect your desired output, and use them as ground truth to evaluate the responses from another model.
 
-![Screenshot of AI-generated evaluation data](../media/ai-generated-test-data.png)
+![Screenshot of AI-generated evaluation data.](../media/ai-generated-test-data.png)
 
 ## Evaluation metrics