Merge pull request #211411 from HeidiSteen/heidist-fresh

[azure search] Misc edits on recently revised content

Author: prmerger-automator[bot]

articles/search/cognitive-search-output-field-mapping.md

@@ -30,11 +30,11 @@ In contrast with a [`fieldMappings`](search-indexer-field-mappings.md) definitio
 
 Output field mappings are required if your indexer has an attached [skillset](cognitive-search-working-with-skillsets.md) that creates new information, such as text translation or key phrase extraction. During indexer execution, AI-generated information exists in memory only. To persist this information in a search index, you'll need to tell the indexer where to send the data.
 
-Output field mappings can also be used to retrieve specific nodes in a source document's complex type. If you don't need the full complex structure, you can [flatten individual nodes in a nested data structures](#flattening-information-from-complex-types), and then use an output field mapping to send the output to a string collection in your search index.
+Output field mappings can also be used to retrieve specific nodes in a source document's complex type. For example, you might want just "FullName/LastName" in a multi-part "FullName" property. When you don't need the full complex structure, you can [flatten individual nodes in a nested data structures](#flattening-information-from-complex-types), and then use an output field mapping to send the output to a string collection in your search index.
 
 Output field mappings apply to:
 
-+ Content that's created by skills or extracted by an indexer. The source field is a node in an enriched document residing in memory.
++ In-memory content that's created by skills or extracted by an indexer. The source field is a node in an enriched document tree.
 
 + Search indexes. If you're populating a [knowledge store](knowledge-store-concept-intro.md), use [projections](knowledge-store-projections-examples.md) for data path configuration.
 

articles/search/search-how-to-create-search-index.md

@@ -9,7 +9,7 @@ ms.author: heidist
 
 ms.service: cognitive-search
 ms.topic: how-to
-ms.date: 06/24/2022
+ms.date: 09/15/2022
 ---
 
 # Create an index in Azure Cognitive Search
@@ -91,7 +91,9 @@ The following screenshot highlights where **Add index** and **Import data** appe
 
 ### [**REST**](#tab/index-rest)
 
-[**Create Index (REST)**](/rest/api/searchservice/create-index) is used to create an index. Both Postman and Visual Studio Code (with an extension for Azure Cognitive Search) can function as a search index client. Using either tool, you can connect to your search service and send requests:
+[**Create Index (REST API)**](/rest/api/searchservice/create-index) is used to create an index. Both Postman and Visual Studio Code (with an extension for Azure Cognitive Search) can function as a search index client. Using either tool, you can connect to your search service and send requests. 
+
+The following links show you how to set up the request:
 
 + [Create a search index using REST and Postman](search-get-started-rest.md)
 + [Get started with Visual Studio Code and Azure Cognitive Search](search-get-started-vs-code.md)

articles/search/search-indexer-field-mappings.md

@@ -16,7 +16,7 @@ ms.date: 09/14/2022
 
 ![Indexer Stages](./media/search-indexer-field-mappings/indexer-stages-field-mappings.png "indexer stages")
 
-When using an [Azure Cognitive Search indexer](search-indexer-overview.md) to pull content into a search index, the indexer can often implicitly detect and assign source-to-destination field mappings. Implicit field mappings occur when field names and data types are compatible. 
+When an [Azure Cognitive Search indexer](search-indexer-overview.md) loads a search index, it determines the data path  through source-to-destination field mappings. Implicit field mappings are internal and occur when field names and data types are compatible between the source and destination. 
 
 If inputs and outputs don't match, you can define explicit *field mappings* to set up the data path, as described in this article. Field mappings can also be used to introduce light-weight data conversion, such as encoding or decoding, through [mapping functions](#mappingFunctions). If more processing is required, consider [Azure Data Factory](../data-factory/index.yml) to bridge the gap.