Semantic search
Semantic search retrieves documents based on meaning rather than keyword overlap, using embedding vectors and similarity scoring to match queries to documents that express the same concept in different words. 'how do I fix a slow Postgres query' matches a document titled 'optimising database performance' even with no keyword overlap.
Semantic search complements (rather than replaces) keyword search. Hybrid approaches, keyword search for exact-match queries, semantic search for natural-language queries, with BM25-style re-ranking, outperform either alone. The implementation is straightforward with modern tooling: embed the corpus once, embed queries at runtime, retrieve nearest vectors. The hard parts are scale (vector databases for large corpora), freshness (re-embedding when documents change), and relevance tuning (most production setups need re-ranking on top of pure vector similarity). Semantic search has become the default retrieval mode for documentation sites, support knowledge bases, and most RAG implementations.