Fixed blocking issues

Author: HeidiSteen

articles/search/tutorial-rag-build-solution-index-schema.md

@@ -1,5 +1,5 @@
 ---
-title: 'RAG Tutorial: Design an index'
+title: 'RAG tutorial: Design an index'
 titleSuffix: Azure AI Search
 description: Design an index for RAG patterns in Azure AI Search.
 

articles/search/tutorial-rag-build-solution-maximize-relevance.md

@@ -1,5 +1,5 @@
 ---
-title: 'RAG Tutorial: Relevance tuning'
+title: 'RAG tutorial: Tune relevance'
 titleSuffix: Azure AI Search
 description: Learn how to use the relevance tuning capabilities to return high quality results for generative search.
 
@@ -16,15 +16,15 @@ ms.date: 10/05/2024
 
 In this tutorial, learn how to improve the relevance of search results used in RAG solutions. Relevance tuning can be an important factor in delivering a RAG solution that meets user expectations. In Azure AI Search, relevance tuning includes L2 semantic ranking and scoring profiles. 
 
-To implement some of these techniques, you revisit the index schema to add configurations for semantic ranking and scoring profiles. Other relevance tuning techniques can be applied to the queries themselves.
+To implement these capabilities, you revisit the index schema to add configurations for semantic ranking and scoring profiles. You then rerun the queries using the new constructs.
 
 In this tutorial, you modify the existing search index and queries to use:
 
 > [!div class="checklist"]
 > - L2 semantic ranking
 > - Scoring profile for document boosting
 
-This tutorial  updates the search index created by the [indexing pipeline](tutorial-rag-build-solution-pipeline.md). Updates don't affect the existing content, so no rebuild is necessary and you don't need to rerun the indexer.
+This tutorial updates the search index created by the [indexing pipeline](tutorial-rag-build-solution-pipeline.md). Updates don't affect the existing content, so no rebuild is necessary and you don't need to rerun the indexer.
 
 > [!NOTE]
 > There are more relevance features in preview, including vector query weighting and setting minimum thresholds, but we omit them from this tutorial becaues they aren't yet available in the Azure SDK for Python.
@@ -122,7 +122,7 @@ In a previous tutorial, you [designed an index schema](tutorial-rag-build-soluti
 
 ## Update queries for semantic ranking and scoring profiles
 
-In a previous tutorial, you [ran queries](tutorial-rag-build-solution-query.md) that execute on the search engine, and pass the response and other information to an LLM for chat completion.
+In a previous tutorial, you [ran queries](tutorial-rag-build-solution-query.md) that execute on the search engine, passing the response and other information to an LLM for chat completion.
 
 This example modifies the query request to include the semantic configuration and scoring profile.
 

articles/search/tutorial-rag-build-solution-pipeline.md

@@ -1,5 +1,5 @@
 ---
-title: 'RAG Tutorial: Build an indexing pipeline'
+title: 'RAG tutorial: Build an indexing pipeline'
 titleSuffix: Azure AI Search
 description: Create an indexer-driven pipeline that loads, chunks, embeds, and ingests content for RAG solutions on Azure AI Search.
 

articles/search/tutorial-rag-build-solution-query.md

@@ -1,5 +1,5 @@
 ---
-title: 'RAG Tutorial: Search using an LLM'
+title: 'RAG tutorial: Search using an LLM'
 titleSuffix: Azure AI Search
 description: Learn how to build queries and engineer prompts for LLM-enabled search on Azure AI Search. Queries used in generative search provide the inputs to an LLM chat engine.
 
@@ -132,7 +132,7 @@ It's expected for LLMs to return different answers, even if the prompt and queri
 
 ## Add a filter
 
-Recall that you created a `locations` field using applied AI, populated with places recognized by the Entity Recognition skill. The field definition for locations includes the `filterable` attribute. Let's repeat the previous request, but this time adding a filter that selects on the term *ice* in the locations field. A filter introduces inclusion or exclusion criteria. The search engine is still doing a vector search on `"how much of earth is covered by water"`, but it's now excluding matches that don't include *ice*. For more information about filtering on string collections and on vector queries, see [text filter fundamentals](search-filters.md#text-filter-fundamentals),[Understand collection filters](search-query-understand-collection-filters.md), and [Add filters to a vector query](vector-search-filters.md).
+Recall that you created a `locations` field using applied AI, populated with places recognized by the Entity Recognition skill. The field definition for locations includes the `filterable` attribute. Let's repeat the previous request, but this time adding a filter that selects on the term *ice* in the locations field. A filter introduces inclusion or exclusion criteria. The search engine is still doing a vector search on `"how much of earth is covered by water"`, but it's now excluding matches that don't include *ice*. For more information about filtering on string collections and on vector queries, see [text filter fundamentals](search-filters.md#text-filter-fundamentals), [Understand collection filters](search-query-understand-collection-filters.md), and [Add filters to a vector query](vector-search-filters.md).
 
 Replace the search_results definition with the following example that includes a filter: