freshness pass

Author: HeidiSteen

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

@@ -11,7 +11,7 @@ ms.custom:
   - ignite-2023
   - ignite-2024
 ms.topic: conceptual
-ms.date: 09/03/2024
+ms.date: 12/18/2024
 ---
 
 # Retrieval Augmented Generation (RAG) in Azure AI Search

articles/search/search-language-support.md

@@ -15,7 +15,7 @@ ms.date: 12/10/2024
 
 # Create an index for multiple languages in Azure AI Search
 
-If you have strings in multiple languages, you can attach [language analyzers](index-add-language-analyzers.md#supported-language-analyzers) that analyze strings using linguistic rules of a specific language during indexing and query execution. With a language analyzer, you get better handling of character variations, punctuation, and word root forms. 
+If you have strings in multiple languages, you can attach [language analyzers](index-add-language-analyzers.md#supported-language-analyzers) that analyze strings using linguistic rules of a specific language during indexing and query execution. With a language analyzer, you get better handling of diacritics, character variants, punctuation, and word root forms.
 
 Azure AI Search supports Microsoft and Lucene analyzers. By default, the search engine uses Standard Lucene, which is language agnostic. If testing indicates that the default analyzer is insufficient, replace it with a language analyzer.
 

articles/search/search-what-is-azure-search.md

@@ -15,11 +15,11 @@ ms.date: 12/10/2024
 
 # What's Azure AI Search?
 
-Azure AI Search ([formerly known as "Azure Cognitive Search"](whats-new.md#new-service-name)) is an enterprise-ready information retrieval system for your heterogeneous content that you ingest into a search index. It comes with a comprehensive set of advanced search technologies, built for high-performance applications at any scale.
+Azure AI Search ([formerly known as "Azure Cognitive Search"](whats-new.md#new-service-name)) is an enterprise-ready information retrieval system for your heterogeneous content that you ingest into a search index, and surface to users through queries and apps. It comes with a comprehensive set of advanced search technologies, built for high-performance applications at any scale.
 
-Azure AI Search is the recommended retrieval system for building RAG-based applications on Azure, with native LLM integrations between Azure OpenAI Service and Azure Machine Learning, and multiple strategies for relevance tuning.
+Azure AI Search is the recommended retrieval system for building RAG-based applications on Azure, with native LLM integrations between Azure OpenAI Service and Azure Machine Learning, an integration mechanism for non-native models and processes, and multiple strategies for relevance tuning.
 
-Azure AI Search can be used in both traditional and GenAI scenarios. Common use cases include catalog or document search, information discovery (data exploration), and retrieval-augmented generation (RAG) for conversational search.  
+Azure AI Search can be used in both traditional and GenAI search scenarios. Common use cases include catalog or document search, information discovery (data exploration), and retrieval-augmented generation (RAG) for conversational search.  
 
 When you create a search service, you work with the following capabilities:
 

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

@@ -8,19 +8,19 @@ author: HeidiSteen
 ms.author: heidist
 ms.service: azure-ai-search
 ms.topic: tutorial
-ms.date: 10/04/2024
+ms.date: 12/18/2024
 
 ---
 
 # Tutorial: Design an index for RAG in Azure AI Search
 
-An index contains searchable text and vector content, plus configurations. In a RAG pattern that uses a chat model for responses, you want an index that contains chunks of content that can be passed to an LLM at query time. 
+An index contains searchable text and vector content, plus configurations. In a RAG pattern that uses a chat model for responses, you want an index designed around chunks of content that can be passed to an LLM at query time. 
 
 In this tutorial, you:
 
 > [!div class="checklist"]
 > - Learn the characteristics of an index schema built for RAG
-> - Create an index that accommodate vectors and hybrid queries
+> - Create an index that accommodates vector and hybrid queries
 > - Add vector profiles and configurations
 > - Add structured data
 > - Add filtering