Update eval tutorial order (#46343)

Author: gewarren

docs/ai/quickstarts/evaluate-ai-response.md

@@ -6,7 +6,7 @@ ms.topic: quickstart
 ms.custom: devx-track-dotnet, devx-track-dotnet-ai
 ---
 
-# Evaluate the quality of a model's response
+# Quickstart: Evaluate response quality
 
 In this quickstart, you create an MSTest app to evaluate the quality of a chat response from an OpenAI model. The test app uses the [Microsoft.Extensions.AI.Evaluation](https://www.nuget.org/packages/Microsoft.Extensions.AI.Evaluation) libraries.
 

docs/ai/toc.yml

@@ -81,12 +81,14 @@ items:
   items:
   - name: The Microsoft.Extensions.AI.Evaluation libraries
     href: conceptual/evaluation-libraries.md
-  - name: "Quickstart: Evaluate the quality of a response"
-    href: quickstarts/evaluate-ai-response.md
-  - name: "Tutorial: Evaluate the safety of a response"
-    href: tutorials/evaluate-safety.md
-  - name: "Tutorial: Evaluate a response with caching and reporting"
-    href: tutorials/evaluate-with-reporting.md
+  - name: Tutorials
+    items:
+    - name: "Quickstart: Evaluate the quality of a response"
+      href: quickstarts/evaluate-ai-response.md
+    - name: "Evaluate response quality with caching and reporting"
+      href: tutorials/evaluate-with-reporting.md
+    - name: "Evaluate response safety with caching and reporting"
+      href: tutorials/evaluate-safety.md
 - name: Resources
   items:
   - name: API reference

docs/ai/tutorials/evaluate-safety.md

@@ -1,12 +1,12 @@
 ---
-title: Tutorial - Evaluate the content safety of a model's response
-description: Create an MSTest app that evaluates the content safety of a model's response using the evaluators in the Microsoft.Extensions.AI.Evaluation.Safety package.
+title: Tutorial - Evaluate response safety with caching and reporting
+description: Create an MSTest app that evaluates the content safety of a model's response using the evaluators in the Microsoft.Extensions.AI.Evaluation.Safety package and with caching and reporting.
 ms.date: 05/12/2025
 ms.topic: tutorial
 ms.custom: devx-track-dotnet-ai
 ---
 
-# Tutorial: Evaluate the content safety of a model's response
+# Tutorial: Evaluate response safety with caching and reporting
 
 In this tutorial, you create an MSTest app to evaluate the *content safety* of a response from an OpenAI model. Safety evaluators check for presence of harmful, inappropriate, or unsafe content in a response. The test app uses the safety evaluators from the [Microsoft.Extensions.AI.Evaluation.Safety](https://www.nuget.org/packages/Microsoft.Extensions.AI.Evaluation.Safety) package to perform the evaluations. These safety evaluators use the [Azure AI Foundry](/azure/ai-foundry/) Evaluation service to perform evaluations.
 

docs/ai/tutorials/evaluate-with-reporting.md

@@ -1,14 +1,14 @@
 ---
-title: Tutorial - Evaluate a model's response
-description: Create an MSTest app and add a custom evaluator to evaluate the AI chat response of a language model, and learn how to use the caching and reporting features of Microsoft.Extensions.AI.Evaluation.
+title: Tutorial - Evaluate response quality with caching and reporting
+description: Create an MSTest app to evaluate the response quality of a language model, add a custom evaluator, and learn how to use the caching and reporting features of Microsoft.Extensions.AI.Evaluation.
 ms.date: 05/09/2025
 ms.topic: tutorial
 ms.custom: devx-track-dotnet-ai
 ---
 
-# Tutorial: Evaluate a model's response with response caching and reporting
+# Tutorial: Evaluate response quality with caching and reporting
 
-In this tutorial, you create an MSTest app to evaluate the chat response of an OpenAI model. The test app uses the [Microsoft.Extensions.AI.Evaluation](https://www.nuget.org/packages/Microsoft.Extensions.AI.Evaluation) libraries to perform the evaluations, cache the model responses, and create reports. The tutorial uses both built-in and custom evaluators.
+In this tutorial, you create an MSTest app to evaluate the chat response of an OpenAI model. The test app uses the [Microsoft.Extensions.AI.Evaluation](https://www.nuget.org/packages/Microsoft.Extensions.AI.Evaluation) libraries to perform the evaluations, cache the model responses, and create reports. The tutorial uses both built-in and custom evaluators. The built-in quality evaluators (from the [Microsoft.Extensions.AI.Evaluation.Quality package](https://www.nuget.org/packages/Microsoft.Extensions.AI.Evaluation.Quality)) use an LLM to perform evaluations; the custom evaluator does not use AI.
 
 ## Prerequisites