corrections to a feature name

Author: HeidiSteen

articles/search/index.yml

@@ -1,10 +1,10 @@
 ### YamlMime:Landing
 
 title: Azure AI Search documentation # < 60 chars
-summary: Information retrieval at scale for agentic RAG, with vector and text content in traditional or generative search scenarios.
+summary: Information retrieval at scale for agentic retrieval, with vector and text content in traditional or generative search scenarios.
 metadata:
   title: Azure AI Search documentation
-  description: Information retrieval at scale for agentic RAG, with vector and text content in traditional or generative search scenarios.
+  description: Information retrieval at scale for agentic retrieval, with vector and text content in traditional or generative search scenarios.
   ms.service: azure-ai-search
   ms.custom:
     - ignite-2023
@@ -43,13 +43,13 @@ landingContent:
             url: vector-search-how-to-query.md
 
   # Card
-  - title: Agentic RAG
+  - title: Agentic retrieval and vectors
     linkLists:
       - linkListType: concept
         links:
           - text: Retrieval Augmented Generation (RAG)
             url: retrieval-augmented-generation-overview.md
-          - text: Agentic RAG
+          - text: Agentic retrieval
             url: search-agentic-retrieval-concept.md        
           - text: Vector search
             url: vector-search-overview.md

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

@@ -58,7 +58,7 @@ A high-level summary of the pattern looks like this:
 + Send it to Azure AI Search to find relevant information.
 + Return the top ranked search results to an LLM.
 + Use the natural language understanding and reasoning capabilities of the LLM to generate a response to the initial prompt.
-+ Optionally, use agentic RAG where an agent evaluates an answer and finds a better one if the original answer is incomplete or low quality.
++ Optionally, use agentic retrieival where an agent evaluates an answer and finds a better one if the original answer is incomplete or low quality.
 
 Azure AI Search provides inputs to the LLM prompt, but doesn't train the model. In RAG architecture, there's no extra training. The LLM is pretrained using public data, but it generates responses that are augmented by information from the retriever, in this case, Azure AI Search.
 

articles/search/search-document-level-access-overview.md

@@ -54,7 +54,7 @@ Document-level permissions in Azure AI Search provide a structured framework for
 To help you dive deeper into document-level access control in Azure AI Search, here’s a table of key resources:  
 
 | Functionality                                   | Reference |  Sample |
-|---|---|  
+|---|---|---|
 | **Index permissions using Push APIs**           | [Index permissions using REST API](search-index-access-control-lists-and-rbac-push-api.md)  |  [azure-search-rest-samples/Quickstart-ACL](https://github.com/Azure-Samples/azure-search-rest-samples-pr/tree/main/Quickstart-ACL) or [azure-search-python-samples/Quickstart-ACL](https://github.com/Azure-Samples/azure-search-python-samples-pr/blob/main/Quickstart-ACL) |
 | **Index ADLS Gen2 permissions metadata using built-in indexers** | [Index permissions using ADLS Gen2 indexer](search-indexer-access-control-lists-and-role-based-access.md) |   [azure-search-python-samples/Quickstart-Document-Permissions](https://github.com/Azure-Samples/azure-search-python-samples/blob/main/Quickstart-Document-Permissions) |
 | **Query using Microsoft Entra token-based permissions** | [Query using Microsoft Entra token-based permissions](https://aka.ms/azs-query-preserving-permissions) | See previous samples. |