fit and finish

Author: HeidiSteen

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

@@ -16,7 +16,7 @@ ms.date: 07/18/2025
 
 # What's Azure AI Search?
 
-Azure AI Search is a knowledge retrieval platform that consolidates and organizes information across different types of content. You add your content to a search index. Users, agents, and bots can retrieve your content through queries and apps.
+Azure AI Search is a knowledge retrieval platform that consolidates and organizes information across different types of content. You add your content to a search index. Users, agents, and bots retrieve your content through queries and apps.
 Indexing and query workloads support native integration with AI models from Azure OpenAI, Azure AI Foundry, and Azure Machine Learning. By leveraging an extensibility layer, you can connect workloads to third-party and open-source AI models and tools.
 
 You can use Azure AI Search for regular search needs (like searching through catalogs or documents) or for AI-powered search that can have conversations with users and generate answers based on your content.
@@ -44,7 +44,9 @@ Architecturally, a search service sits between the external data stores that con
 
 ![Azure AI Search architecture](media/search-what-is-azure-search/azure-search.svg "Azure AI Search architecture")
 
-If your content is on Azure, you can used indexers and skillsets for automated and AI-enriched indexing. Or, create a logic app workflow for equivalent automation over an even broader set of supported data sources. On the retrieval side, Your app can be an agent or tool, a bot, or any client that sends requests to a search index or knowledge agent.
+If your content is on Azure, you can used indexers and skillsets for automated and AI-enriched indexing. Or, create a logic app workflow for equivalent automation over an even broader set of supported data sources. 
+
+On the retrieval side, your app can be an agent or tool, a bot, or any client that sends requests to a search index or knowledge agent.
 
 ## Inside a search service
 
@@ -58,25 +60,23 @@ On the search service itself, the two primary workloads are *indexing* and *quer
 
 ## Why use Azure AI Search?
 
-Azure AI Search is well suited for the following application scenarios:
+Azure AI Search offloads indexing and query workloads onto a dedicated search service. It's well suited for the following application scenarios:
 
 + Use it for empowering agents and bots with grounding data based on your content.
 
 + Use it for traditional full text search and next-generation vector similarity search. Back your generative AI apps with information retrieval that leverages the strengths of both keyword and similarity search. Use both modalities to retrieve the most relevant results.
 
 + Consolidate heterogeneous content into a user-defined and populated search index composed of vectors and text. You maintain ownership and control over what's searchable.
 
-+ [Integrate data chunking and vectorization](vector-search-integrated-vectorization.md) for generative AI and RAG apps.
++ Transform large undifferentiated text or image files, or application files stored in Azure Blob Storage or Azure Cosmos DB, into searchable chunks. This is achieved during indexing through [AI skills](cognitive-search-concept-intro.md) that add external processing from Azure AI.
 
-+ [Apply granular access control](https://techcommunity.microsoft.com/t5/azure-ai-services-blog/access-control-in-generative-ai-applications-with-azure/ba-p/3956408) at the document level.
++ [Integrate data chunking and vectorization](vector-search-integrated-vectorization.md) for generative AI and RAG apps.
 
-+ Offload indexing and query workloads onto a dedicated search service.
++ Add linguistic or custom text analysis for keyword search. If you have non-English content, Azure AI Search supports both Lucene analyzers and Microsoft's natural language processors. You can also configure analyzers to achieve specialized processing of raw content, such as filtering out diacritics, or recognizing and preserving patterns in strings.
 
 + Easily implement search-related features: relevance tuning, faceted navigation, filters (including geo-spatial search), synonym mapping, and autocomplete.
 
-+ Transform large undifferentiated text or image files, or application files stored in Azure Blob Storage or Azure Cosmos DB, into searchable chunks. This is achieved during indexing through [AI skills](cognitive-search-concept-intro.md) that add external processing from Azure AI.
-
-+ Add linguistic or custom text analysis. If you have non-English content, Azure AI Search supports both Lucene analyzers and Microsoft's natural language processors. You can also configure analyzers to achieve specialized processing of raw content, such as filtering out diacritics, or recognizing and preserving patterns in strings.
++ [Apply granular access control](https://techcommunity.microsoft.com/t5/azure-ai-services-blog/access-control-in-generative-ai-applications-with-azure/ba-p/3956408) at the document level.
 
 For more information about specific functionality, see [Features of Azure AI Search](search-features-list.md)
 
@@ -121,13 +121,13 @@ Alternatively, you can create, load, and query a search index in atomic steps:
 
 Or, try solution accelerators:
 
-+ [**Chat with your data** solution accelerator](https://github.com/Azure-Samples/chat-with-your-data-solution-accelerator) helps you create a custom RAG solution over your content.
++ [**Chat with your data solution accelerator**](https://github.com/Azure-Samples/chat-with-your-data-solution-accelerator) helps you create a custom RAG solution over your content.
 
-+ [**Conversational Knowledge Mining** solution accelerator](https://github.com/microsoft/Customer-Service-Conversational-Insights-with-Azure-OpenAI-Services) helps you create an interactive solution to extract actionable insights from post-contact center transcripts.
++ [**Conversational Knowledge Mining solution accelerator**](https://github.com/microsoft/Customer-Service-Conversational-Insights-with-Azure-OpenAI-Services) helps you create an interactive solution to extract actionable insights from post-contact center transcripts.
 
 + [**Document Knowledge Mining accelerator**](https://github.com/microsoft/Document-Knowledge-Mining-Solution-Accelerator) helps you process and extract summaries, entities, and metadata from unstructured, multimodal documents.
 
-+ [**Build your own copilot** solution accelerator](https://github.com/microsoft/Build-your-own-copilot-Solution-Accelerator), leverages Azure OpenAI, Azure AI Search and Microsoft Fabric, to create custom copilot solutions.
++ [**Build your own copilot solution accelerator**](https://github.com/microsoft/Build-your-own-copilot-Solution-Accelerator), leverages Azure OpenAI, Azure AI Search and Microsoft Fabric, to create custom copilot solutions.
 
 <!--   + [Generic copilot](https://github.com/microsoft/Generic-Build-your-own-copilot-Solution-Accelerator) helps you build your own copilot to identify relevant documents, summarize unstructured information, and generate Word document templates using your own data.