more edits

Author: HeidiSteen

articles/search/cognitive-search-concept-intro.md

@@ -10,19 +10,21 @@ ms.service: cognitive-search
 ms.custom:
   - ignite-2023
 ms.topic: conceptual
-ms.date: 10/27/2023
+ms.date: 01/30/2024
 ---
 # AI enrichment in Azure AI Search
 
-In Azure AI Search, *AI enrichment* calls the APIs of [Azure AI services](/azure/ai-services/what-are-ai-services) to process content that isn't full text searchable in its raw form. Through enrichment, analysis and inference are used to create searchable content and structure where none previously existed. 
+In Azure AI Search, *AI enrichment* refers to integration with [Azure AI services](/azure/ai-services/what-are-ai-services) to process content that isn't searchable in its raw form. Through enrichment, analysis and inference are used to create searchable content and structure where none previously existed. 
 
-Because Azure AI Search is a full text search solution, the purpose of AI enrichment is to improve the utility of your content in search-related scenarios:
+Because Azure AI Search is a text and vector search solution, the purpose of AI enrichment is to improve the utility of your content in search-related scenarios. Source content must be textual (you can't enrich vectors), but the content created by an enrichment pipeline can be vectorized and indexed in a vector store using skills like [Text Split skill](cognitive-search-skill-textsplit.md) for chunking and [AzureOpenAiEmbedding skill](cognitive-search-skill-azure-openai-embedding.md) for encoding.
 
-+ Apply translation and language detection for multi-lingual search
-+ Apply entity recognition to extract people names, places, and other entities from large chunks of text
-+ Apply key phrase extraction to identify and output important terms
-+ Apply Optical Character Recognition (OCR) to recognize printed and handwritten text in binary files
-+ Apply image analysis to describe image content, and output the descriptions as searchable text fields
+Built-in skills apply the following transformation and processing to raw content:
+
++ Translation and language detection for multi-lingual search
++ Entity recognition to extract people names, places, and other entities from large chunks of text
++ Key phrase extraction to identify and output important terms
++ Optical Character Recognition (OCR) to recognize printed and handwritten text in binary files
++ Image analysis to describe image content, and output the descriptions as searchable text fields
 
 AI enrichment is an extension of an [**indexer pipeline**](search-indexer-overview.md) that connects to Azure data sources. An enrichment pipeline has all of the components of an indexer pipeline (indexer, data source, index), plus a [**skillset**](cognitive-search-working-with-skillsets.md) that specifies atomic enrichment steps.
 

articles/search/knowledge-store-concept-intro.md

@@ -14,13 +14,13 @@ ms.date: 01/10/2024
 
 # Knowledge store in Azure AI Search
 
-Knowledge store is secondary storage for [AI-enriched content created by a skillset](cognitive-search-concept-intro.md) in Azure AI Search. In Azure AI Search, an indexing job always sends output to a search index, but if you attach a skillset to an indexer, you can optionally also send AI-enriched output to a container or table in Azure Storage. A knowledge store can be used for independent analysis or downstream processing in non-search scenarios like knowledge mining.
+Knowledge store is secondary storage for [AI-enriched content created by a skillset](cognitive-search-concept-intro.md) in Azure AI Search. In Azure AI Search, an indexing job always sends output to a search index, but if you attach a skillset to an indexer, you can optionally also send AI-enriched output to a container or table in Azure Storage. A knowledge store can be used for independent analysis or downstream processing in non-search scenarios like knowledge mining. 
 
 The two outputs of indexing, a search index and knowledge store, are mutually exclusive products of the same pipeline. They're derived from the same inputs and contain the same data, but their content is structured, stored, and used in different applications.
 
 :::image type="content" source="media/knowledge-store-concept-intro/knowledge-store-concept-intro.svg" alt-text="Pipeline with skillset" border="false":::
 
-Physically, a knowledge store is [Azure Storage](../storage/common/storage-account-overview.md), either Azure Table Storage, Azure Blob Storage, or both. Any tool or process that can connect to Azure Storage can consume the contents of a knowledge store.
+Physically, a knowledge store is [Azure Storage](../storage/common/storage-account-overview.md), either Azure Table Storage, Azure Blob Storage, or both. Any tool or process that can connect to Azure Storage can consume the contents of a knowledge store. There's no query support in Azure AI Search for retrieving content from a knowledge store.
 
 When viewed through Azure portal, a knowledge store looks like any other collection of tables, objects, or files. The following screenshot shows a knowledge store composed of three tables. You can adopt a naming convention, such as a `kstore` prefix, to keep your content together.