relevance tuning

Author: HeidiSteen

articles/search/search-relevance-overview.md

@@ -17,10 +17,12 @@ 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.
 
-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.
+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 approach outperforms the other. 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
 
+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 |