Concept article placement

Author: HeidiSteen

articles/search/search-relevance-overview.md

@@ -17,7 +17,7 @@ In a query operation, the relevance of any given result is measured by a ranking
 
 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.
 
-In Azure AI Search, ***relevance tuning*** is primarily centered on textual content, applying scoring profiles or semantic ranking to enhance the quality of results. For vectors, 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 testing. You can also experiment with various embedding models to see which ones produce higher quality results.
+***Relevance tuning*** is primarily centered on textual content, applying scoring profiles or semantic ranking to enhance the quality of results. For vectors, 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 testing. You can also experiment with various embedding models to see which ones produce higher quality results.
 
 ## Levels of ranking
 

articles/search/search-what-is-an-index.md

@@ -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:
 

articles/search/toc.yml

@@ -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