Can an LLM and a keyword index replace embeddings?

An engineer evaluated whether combining a large language model query rewrite and a BM25 keyword index could replace vector embeddings for semantic search across multiple public datasets. The experiments found that embeddings still consistently outperform keyword search when answers use different vocabulary or descriptions, while keyword search performs competitively only when exact terms or rare codenames match. Open article →