Improve Search Relevance with Semantic Search — Natural Language Queries

Quick Answer

The relevance problem here comes from a mismatch between how the index scores results and how users are actually searching: default keyword-based search ranks documents by literal term matches, which works fine for short keyword queries but breaks down when users type full natural-language phrases, since the exact wording someone uses to ask a question rarely matches the exact wording in the source document even when the document answers it perfectly. Semantic search fixes this by adding a re-ranking layer on top of the existing index — it uses language models to understand the intent behind the query and re-scores the initial keyword results based on semantic relevance to that intent, surfacing documents that actually answer the question even when their wording differs from the query. What makes this the right fix for the stated constraint is that it's a configuration change on the existing index rather than an application rewrite: enabling semantic search and defining a semantic configuration adjusts how results are ranked without requiring any change to how the application queries or displays them. Any scenario describing poor relevance specifically for natural-language or conversational queries, where a config-only fix is required, is pointing at semantic search rather than a change to the underlying index schema or application code.