Merge pull request #4080 from eric-urban/eur/ai-search-update

edit

Author: prmerger-automator[bot]

articles/search/semantic-search-overview.md

@@ -24,13 +24,13 @@ Semantic ranker is a premium feature, billed by usage. We recommend this article
 
 ## What is semantic ranking?
 
-Semantic ranker calls LLMs at query time. LLms are used to improve the quality of an initial [BM25-ranked](index-similarity-and-scoring.md) or [RRF-ranked](hybrid-search-ranking.md) search result for text-based queries, the text portion of vector queries, and hybrid queries. Semantic ranking extends the query execution pipeline in three ways:
+Semantic ranker is a collection of query-side capabilities that improve the quality of an initial [BM25-ranked](index-similarity-and-scoring.md) or [RRF-ranked](hybrid-search-ranking.md) search result for text-based queries, the text portion of vector queries, and hybrid queries. Semantic ranking extends the query execution pipeline in three ways:
 
 * First, it always adds secondary ranking over an initial result set that was scored using BM25 or Reciprocal Rank Fusion (RRF). This secondary ranking uses multi-lingual, deep learning models adapted from Microsoft Bing to promote the most semantically relevant results.
 
 * Second, it returns captions and optionally extracts answers in the response, which you can render on a search page to improve the user's search experience.
 
-* Third, if you enable query rewrite, it calls an LLM to expand an initial query string into multiple semantically similar query strings. 
+* Third, if you enable query rewrite, it expands an initial query string into multiple semantically similar query strings. 
 
 Secondary ranking and "answers" apply to the query response. Query rewrite is part of the query request.
 
@@ -41,7 +41,7 @@ Here are the capabilities of the semantic reranker.
 | L2 ranking | Uses the context or semantic meaning of a query to compute a new relevance score over preranked results. |
 | [Semantic captions and highlights](semantic-how-to-query-request.md) | Extracts verbatim sentences and phrases from fields that best summarize the content, with highlights over key passages for easy scanning. Captions that summarize a result are useful when individual content fields are too dense for the search results page. Highlighted text elevates the most relevant terms and phrases so that users can quickly determine why a match was considered relevant. |
 | [Semantic answers](semantic-answers.md) | An optional and extra substructure returned from a semantic query. It provides a direct answer to a query that looks like a question. It requires that a document has text with the characteristics of an answer. |
-| [Query rewrite](semantic-how-to-query-rewrite.md) | Using text queries or the text portion of a vector query, semantic ranker creates up to 10 variants of the query, perhaps correcting typos or spelling errors, or rephrasing a query using synonyms generated by the LLM. The rewritten query runs on the search engine. The results are scored using BM25 or RRF scoring, and then rescored by semantic ranker.  |
+| [Query rewrite](semantic-how-to-query-rewrite.md) | Using text queries or the text portion of a vector query, semantic ranker creates up to 10 variants of the query, perhaps correcting typos or spelling errors, or rephrasing a query using generated synonyms. The rewritten query runs on the search engine. The results are scored using BM25 or RRF scoring, and then rescored by semantic ranker.  |
 
 ## How semantic ranker works