fixed validation warning

Author: HeidiSteen

articles/search/agentic-retrieval-how-to-create-pipeline.md

@@ -20,10 +20,11 @@ This article describes an approach or pattern for building a solution that uses
 
 :::image type="content" source="media/agentic-retrieval/agent-to-agent-pipeline.svg" alt-text="Diagram of Azure AI Search integration with Azure AI Agent service." lightbox="media/agentic-retrieval/agent-to-agent-pipeline.png" :::
 
-To run the code for this tutorial, download the [agentic-retrieval-pipeline-example](https://github.com/Azure-Samples/azure-search-python-samples/tree/main/agentic-retrieval-pipeline-example) Python sample on GitHub.
-
 This exercise differs from the [Agentic Retrieval Quickstart](search-get-started-agentic-retrieval.md) in how it uses Azure AI Agent to retrieve data from the index, and how it uses an agent tool for orchestration. If you want to understand the retrieval pipeline in its simplest form, begin with the quickstart.
 
+< [!TIP]>
+> To run the code for this tutorial, download the [agentic-retrieval-pipeline-example](https://github.com/Azure-Samples/azure-search-python-samples/tree/main/agentic-retrieval-pipeline-example) Python sample on GitHub.
+
 ## Prerequisites
 
 The following resources are required for this design pattern:
@@ -325,7 +326,7 @@ You can also delete individual objects:
 
 + [Delete a knowledge source](agentic-knowledge-source-how-to-search-index.md#delete-a-knowledge-source)
 
-+ [Delete an index](search-how-to-manage-index#delete-an-index)
++ [Delete an index](search-how-to-manage-index.md#delete-an-index)
 
 ## Related content
 

articles/search/retrieval-augmented-generation-overview.md

@@ -35,7 +35,7 @@ You can choose between two approaches for RAG workloads: agentic retrieval, or t
 > [!NOTE]
 > New to copilot and RAG concepts? Watch [Vector search and state of the art retrieval for Generative AI apps](https://www.youtube.com/watch?v=lSzc1MJktAo).
 
-## Option 1: Modern RAG with Agentic Retrieval
+## Modern RAG with Agentic Retrieval
 
 Azure AI Search now provides **agentic retrieval**, a specialized pipeline designed specifically for RAG patterns. This approach uses large language models to intelligently break down complex user queries into focused subqueries, executes them in parallel, and returns structured responses optimized for chat completion models.
 
@@ -51,9 +51,9 @@ You need new objects for this pipeline: one or more knowledge sources, a knowled
 
 For new RAG implementations, we recommend starting with [agentic retrieval](agentic-retrieval-overview.md). For existing solutions, consider migrating to take advantage of improved accuracy and context understanding.
 
-## Option 2: Classic RAG pattern for Azure AI Search
+## Classic RAG pattern for Azure AI Search
 
-A RAG solution can be implemented on Azure AI Search using the original query execution environment. This approach is faster and simpler with fewer components, and depending on your application requirements it can be the best choice. There's no LLM query planning or LLM integration in the query pipeline. Your application sends a single query request to Azure AI Search, the search engine executes the query and returns search results. There's no query execution details in the response, and citations are built into the response only if you have fields in your index that provide a parent document name or page.
+A RAG solution can be implemented on Azure AI Search using the original query execution architecture. With this approach, your application sends a single query request to Azure AI Search, the search engine processes the request, and returns search results to the caller. There's no side trip to an LLM query planning or LLM integration in the query pipeline. There's no query execution details in the response, and citations are built into the response only if you have fields in your index that provide a parent document name or page. This approach is faster and simpler with fewer components. Depending on your application requirements, it can be the best choice. 
 
 A high-level summary of classic RAG pattern built on Azure AI Search looks like this: