Merge pull request #92216 from HeidiSteen/heidist-master

Azure Search: updated art for knowledge store

Author: ktoliver

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

@@ -16,17 +16,17 @@ ms.author: heidist
 > Knowledge store is in preview and not intended for production use. The [REST API version 2019-05-06-Preview](search-api-preview.md) provides this feature. There is no .NET SDK support at this time.
 >
 
-Knowledge store is a feature in Azure Search that persists output from an [AI enrichment pipeline](cognitive-search-concept-intro.md) for later analysis or other downstream processing. An *enriched document* is a pipeline's output, created from content that has been extracted, structured, and analyzed using resources in Cognitive Services. In a standard AI-based pipeline, enriched documents are transitory, used only during indexing and then discarded. With knowledge store, documents are saved for use in other apps or downstream data science workloads. 
+Knowledge store is a feature in Azure Search that persists output from an [AI enrichment pipeline](cognitive-search-concept-intro.md) for later analysis or other downstream processing. An *enriched document* is a pipeline's output, created from content that has been extracted, structured, and analyzed using AI processes. In a standard AI pipeline, enriched documents are transitory, used only during indexing and then discarded. With knowledge store, enriched documents are preserved. 
 
-If you have used AI skills with Azure Search in the past, you already know that *skillsets* are used to move a document through a sequence of enrichments. The outcome can be an Azure Search index, or (new in this preview) projections in a knowledge store. The two outputs, search index and knowledge store, are physically distinct from each other. They share the same content, but are stored and used in very different ways.
+If you have used AI skills with Azure Search in the past, you already know that *skillsets* move a document through a sequence of enrichments. The outcome can be a search index, or (new in this preview) projections in a knowledge store. The two outputs, search index and knowledge store, share the same content, but are stored and used in very different ways.
 
-Physically, a knowledge store is an [Azure Storage account](https://docs.microsoft.com/azure/storage/common/storage-account-overview), either as Azure Table storage, Azure Blob storage, or both, depending on how you configure the pipeline. Any tool or process that can connect to an Azure Storage account can consume the contents of a knowledge store.
+Physically, a knowledge store is [Azure Storage](https://docs.microsoft.com/azure/storage/common/storage-account-overview), 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.
 
-Projections are your mechanism for structuring data in a knowledge store. For example, through projections, you can choose whether output is saved as a single blob or a collection of related tables. An easy way to view knowledge store contents is through the built-in [Storage Explorer](https://docs.microsoft.com/azure/vs-azure-tools-storage-manage-with-storage-explorer?tabs=windows) for Azure storage.
+![Knowledge store in pipeline diagram](./media/knowledge-store-concept-intro/knowledge-store-concept-intro.svg "Knowledge store in pipeline diagram")
 
-![Knowledge store in pipeline diagram](./media/knowledge-store-concept-intro/annotationstore_sans_internalcache.png "Knowledge store in pipeline diagram")
+Projections are your mechanism for structuring data in a knowledge store. For example, through projections, you can choose whether output is saved as a single blob or a collection of related tables. 
 
-To use knowledge store, add a `knowledgeStore` element to a skillset that defines step-wise operations in an indexing pipeline. During execution, Azure Search creates a space in your Azure storage account and projects the enriched documents with the definition created within the pipeline.
+To use knowledge store, add a `knowledgeStore` element to a skillset that defines step-wise operations in an indexing pipeline. During execution, Azure Search creates a space in your Azure storage account and projects the enriched documents as blobs or into tables, depending on your configuration.
 
 ## Benefits of knowledge store