@@ -168,9 +168,10 @@ JSON schema example:
 
 To effectively manage blob deletion, ensure that you have enabled [deletion tracking](search-howto-index-changed-deleted-blobs.md) before your indexer runs for the first time. This feature allows the system to detect deleted blobs from your source and have them deleted from the index.  
 
-## Related content
+## See also
 
++ [Connect to Azure AI Search using roles](search-security-rbac.md)
+- [Query-Time ACL and RBAC enforcement](search-query-access-control-rbac-enforcement.md)
+- [azure-search-python-samples/Quickstart-Document-Permissions-Push-API](https://github.com/Azure-Samples/azure-search-python-samples/blob/main/Quickstart-Document-Permissions-Push-API)
 + [Search over Azure Blob Storage content](search-blob-storage-integration.md)
 + [Configure a blob indexer](search-howto-indexing-azure-blob-storage.md)
-+ [Change and delete detection using indexers for Azure Storage](search-howto-index-changed-deleted-blobs.md)
-+ [Connect to Azure AI Search using roles](search-security-rbac.md)
\ No newline at end of file

@@ -122,7 +122,8 @@ This example illustrates how the document access rules are resolved based on the
 | 6 | ["user1", "user2"] | ["group1"] | Empty | User1, user2, or any member of group1 | |
 | 7 | ["user1", "user2"] | [] | Empty | User1, user2, or any user with RBAC permissions to container1 | |
 
-## Next steps
+## See also
 
+- [Connect to Azure AI Search using roles](search-security-rbac.md)
 - [Query-Time ACL and RBAC enforcement](search-query-access-control-rbac-enforcement.md)
 - [azure-search-python-samples/Quickstart-Document-Permissions-Push-API](https://github.com/Azure-Samples/azure-search-python-samples/blob/main/Quickstart-Document-Permissions-Push-API)

@@ -298,3 +298,8 @@ Choose one of the following mechanisms, depending on how many items changed:
 
 To effectively manage blob deletion, ensure that you have enabled [deletion tracking](search-howto-index-changed-deleted-blobs.md) before your indexer runs for the first time. This feature allows the system to detect deleted blobs from your source and have them deleted from the index.
 
+## See also
+
++ [Connect to Azure AI Search using roles](search-security-rbac.md)
++ [Query-Time ACL and RBAC enforcement](search-query-access-control-rbac-enforcement.md)
++ [azure-search-python-samples/Quickstart-Document-Permissions-Push-API](https://github.com/Azure-Samples/azure-search-python-samples/blob/main/Quickstart-Document-Permissions-Push-API)
\ No newline at end of file

@@ -76,7 +76,7 @@ Content-Type: application/json
 }
 ```
 
-## Related content
+## See also
 
 - [Tutorial: Index ADLS Gen2 permission metadata](tutorial-adls-gen2-indexer-acls.md) 
 

@@ -13,14 +13,19 @@ ms.date: 07/23/2025
 
 # Relevance in Azure AI Search
 
-In a query operation, the relevance of any given result is measured by a ranking algorithm that determines the strength of a match based on how closely the result aligns with the query’s content or characteristics. An algorithm assigns a score, and results are rank ordered by that score, with the most relevant matches returned in the response. 
+In a query operation, the relevance of any given result is determined by a ranking algorithm that evaluates the strength of a match based on how closely the indexed content and the query align. An algorithm assigns a score, and results are ranked by that score and returned in the response. 
 
 Ranking occurs whenever the query request includes full text or vector queries. It doesn't occur if the query invokes strict pattern matching, such as a filter-only query or a specialized query form like autocomplete, suggestions, geospatial search, fuzzy search, or regular expression search. A uniform search score of 1.0 indicates the absence of a ranking algorithm.
 
-The query engine in Azure AI Search supports a multi-level approach to ranking search results, where there's a built-in ranking modality for each query type, plus extra ranking capabilities for extended relevance tuning.
+You can enhance the quality of ranked results through ***relevance tuning*** that boosts search scores based on extra criteria or analysis. In Azure AI Search, relevance tuning is primarily directed at textual and numeric (nonvector) content when you apply a [scoring profile](#custom-boosting-logic-using-scoring-profiles) or invoke the [semantic ranker](semantic-search-overview.md). 
+
+> [!NOTE]
+> In Azure AI Search, there's no explicit relevance tuning capabilities for vector content, but you can experiment between Hierarchical Navigable Small World (HNSW) and exhaustive K-nearest neighbors (KNN) to see if one algorithm outperforms the other for your scenario. HNSW graphing with an exhaustive KNN override at query time is the most flexible approach for comparison testing. You can also experiment with various embedding models to see which ones produce higher quality results.
 
 ## Levels of ranking
 
+The query engine in Azure AI Search supports a multi-level approach to ranking search results, where there's a built-in ranking modality for each query type, plus extra ranking capabilities for extended relevance tuning.
+
 This section describes the levels of scoring operations. For an illustration of how they work together, see the [diagram](#diagram-of-ranking-algorithms) in this article. A [comparison of all search score types and ranges](#types-of-search-scores) is also provided in this article.
 
 | Level | Description |
@@ -31,7 +36,7 @@ This section describes the levels of scoring operations. For an illustration of
 
 ## Custom boosting logic using scoring profiles
 
-[Scoring profiles](index-add-scoring-profiles.md) are an optional feature for boosting scores based on extra user-defined criteria. Criteria can include weighted fields, or functions that boost by freshness, proximity, magnitude, or range. There's no extra charge for using a scoring profile. To use a scoring profile, you define it in an index and then specify it on a query. 
+[Scoring profiles](index-add-scoring-profiles.md) are an optional feature for boosting scores based on extra user-defined criteria. Criteria can include weighted fields where a match found in a specific field is given more weight than the same match found in a different field. Criteria can also be defined through functions that boost by freshness, proximity, magnitude, or range. There's no extra costs associated with scoring profiles. To use a scoring profile, you define it in an index and then specify it on a query. 
 
 Scoring logic applies to text and numeric nonvector content. You can use scoring profiles with:
 

@@ -11,7 +11,7 @@ ms.update-cycle: 180-days
 ms.custom:
   - ignite-2023
 ms.topic: conceptual
-ms.date: 02/28/2025
+ms.date: 07/26/2025
 ---
 
 # Security in Azure AI Search
@@ -155,9 +155,11 @@ For multitenancy solutions requiring security boundaries at the index level, it'
 
 ### Restricting access to documents
 
-User permissions at the document level, also known as *row-level security*, isn't natively supported in Azure AI Search. If you import data from an external system that provides row-level security, such as Azure Cosmos DB, those permissions won't transfer with the data as its being indexed by Azure AI Search.
+User permissions at the document level, also known as *row-level security*, is available as a preview feature and depends on the data source. If content originates from [Azure Data Lake Storage (ADLS) Gen2](search-indexer-access-control-lists-and-role-based-access.md) or [Azure blobs](search-blob-indexer-role-based-access.md), user permission metadata that originates in Azure Storage is preserved in indexer-generated indexes and enforced at query time so that only authorized content is included in search results.
 
-If you require permissioned access over content in search results, there's a technique for applying filters that include or exclude documents based on user identity. This workaround adds a string field in the data source that represents a group or user identity, which you can make filterable in your index. For more information about this pattern, see [Security trimming based on identity filters](search-security-trimming-for-azure-search.md).
+For other data sources, you can [push a document payload that includes user or group permission metadata](search-index-access-control-lists-and-rbac-push-api.md), and those permissions are retained in indexed content and also enforced at query time. This capability is also in preview.
+
+If you can't use preview features and you require permissioned access over content in search results, there's a technique for applying filters that include or exclude documents based on user identity. This workaround adds a string field in the data source that represents a group or user identity, which you can make filterable in your index. For more information about this pattern, see [Security trimming based on identity filters](search-security-trimming-for-azure-search.md). For more information about document access, see [Document-level access control](search-document-level-access-overview.md).
 
 ## Data residency
 

@@ -16,7 +16,7 @@ ms.date: 06/20/2025
 
 # Search indexes in Azure AI Search
 
-In Azure AI Search, a *search index* is your searchable content, available to the search engine for indexing, full-text search, vector search, hybrid search, and filtered queries. An index is defined by a schema and saved to the search service, with data import following as a second step. This content exists within your search service, apart from your primary data stores, which is necessary for the millisecond response times expected in modern search applications. Except for indexer-driven indexing scenarios, the search service never connects to or queries your source data.
+In Azure AI Search, a *search index* is your searchable content, available to the search engine for indexing, agentic search, full-text search, vector search, hybrid search, and filtered queries. An index is defined by a schema and saved to the search service, with data ingestion following as a second step. Indexed content exists within your search service, apart from your primary external data stores, which is necessary for the millisecond response times expected in modern search applications. Except for indexer-driven indexing scenarios, the search service never connects to or queries your external source data.
 
 This article covers the key concepts for creating and managing a search index, including:
 

@@ -18,28 +18,22 @@ items:
   items:
   - name: Data
     items:
+    - name: Data import strategies
+      href: search-what-is-data-import.md
     - name: Search index
       href: search-what-is-an-index.md
     - name: Vector index
       href: vector-store.md
     - name: Knowledge store
       href: knowledge-store-concept-intro.md
-    - name: Data import strategies
-      href: search-what-is-data-import.md
     - name: Indexers
       href: search-indexer-overview.md
   - name: Applied AI
     items:
-    - name: Multimodal search
-      href: multimodal-search-overview.md
-    - name: Built-in vectorization
-      href: vector-search-integrated-vectorization.md
     - name: AI enrichment during indexing
       href: cognitive-search-concept-intro.md
-    - name: Enrichment cache
-      href: cognitive-search-incremental-indexing-conceptual.md
-    - name: Skillsets
-      href: cognitive-search-working-with-skillsets.md
+    - name: Built-in vectorization
+      href: vector-search-integrated-vectorization.md
   - name: Retrieval
     items:
     - name: Agentic search
@@ -50,6 +44,8 @@ items:
       href: vector-search-overview.md
     - name: Hybrid search
       href: hybrid-search-overview.md
+    - name: Multimodal search
+      href: multimodal-search-overview.md
     - name: Retrieval Augmented Generation (RAG)
       href: retrieval-augmented-generation-overview.md
     - name: Other query types
@@ -348,6 +344,8 @@ items:
         href: search-howto-index-sharepoint-online.md
     - name: Skillsets (indexers)
       items:
+      - name: Skillsets overview
+        href: cognitive-search-working-with-skillsets.md
       - name: Create a skillset
         href: cognitive-search-defining-skillset.md
       - name: Attach an Azure AI resource
@@ -364,6 +362,8 @@ items:
         href: cognitive-search-output-field-mapping.md
       - name: Process image files
         href: cognitive-search-concept-image-scenarios.md
+      - name: Enrichment cache
+        href: cognitive-search-incremental-indexing-conceptual.md
       - name: Cache (incremental) enrichment
         href: search-howto-incremental-index.md
       - name: Design tips