Merge pull request #101244 from HeidiSteen/heidist-bugfix

Azure Cog Search - caching (Vinod's edits)

Author: PRMerger12

articles/search/cognitive-search-incremental-indexing-conceptual.md

@@ -53,14 +53,16 @@ The lifecycle of the cache is managed by the indexer. If the `cache` property on
 
 While incremental enrichment is designed to detect and respond to changes with no intervention on your part, there are parameters you can use to override default behaviors:
 
-+ Suspend caching
++ Prioritize new documents
 + Bypass skillset checks
 + Bypass data source checks
 + Force skillset evaluation
 
-### Suspend caching
+### Prioritize new documents
 
-You can temporarily suspend incremental enrichment by setting the `enableReprocessing` property in the cache to `false`, and later resume incremental enrichment and drive eventual consistency by setting it to `true`. This control is particularly useful when you want to prioritize indexing new documents over ensuring consistency across your corpus of documents.
+Set the `enableReprocessing` property to control processing over incoming documents already represented in the cache. When `true` (default), documents already in the cache are reprocessed when you rerun the indexer, assuming your skill update affects that doc. 
+
+When `false`, existing documents are not reprocessed, effectively prioritizing new, incoming content over existing content. You should only set `enableReprocessing` to `false` on a temporary basis. To ensure consistency across the corpus, `enableReprocessing` should be `true` most of the time, ensuring that all documents, both new and existing, are valid per the current skillset definition.
 
 ### Bypass skillset evaluation
 
@@ -90,9 +92,9 @@ PUT https://customerdemos.search.windows.net/datasources/callcenter-ds?api-versi
 
 ### Force skillset evaluation
 
-The purpose of the cache is to avoid unnecessary processing, but suppose you have made a change to a skill or skillset that the indexer doesn't detect (for example, changes to external components like a custom skillset). 
+The purpose of the cache is to avoid unnecessary processing, but suppose you make a change to a skill that the indexer doesn't detect (for example, changing something in external code, such as a custom skill).
 
-In this case, you can use the [Reset Skills](preview-api-resetskills.md) API to force reprocessing of a particular skill, including any downstream skills that have a dependency on that skill's output. This API accepts a POST request with a list of skills that should be invalidated and rerun. After Reset Skills, run the indexer to execute the operation.
+In this case, you can use the [Reset Skills](preview-api-resetskills.md) to force reprocessing of a particular skill, including any downstream skills that have a dependency on that skill's output. This API accepts a POST request with a list of skills that should be invalidated and marked for reprocessing. After Reset Skills, run the indexer to invoke the pipeline.
 
 ## Change detection