BM25 (Best match 25) is a keyword-based ranking algorithm used in search systems to score how relevant a document is to a query. It works by analyzing term frequency, such as how often a search term appears in a document. It balances that that against how common the term is across the entire document collection. Terms that appear frequently in a specific document but rarely across the collection are treated as stronger relevance signals than terms that appear everywhere.
BM25 is one of the most widely used retrieval algorithms in traditional search systems and continues to play a role in modern AI search and retrieval pipelines, often alongside semantic search. Where semantic search finds content by meaning, BM25 finds content by exact and near-exact term matches. Using both together can improve retrieval quality by capturing relevance signals that either method alone might miss.
Why This Matters for Technical Writers
BM25 is relevant to technical writers documenting AI search and retrieval products, particularly those built on hybrid retrieval architectures that combine keyword and semantic search. Understanding that BM25 operates on term matching helps explain why precise, consistent terminology in documentation affects search outcomes. Content that uses the same terms users are likely to query will perform better under BM25 retrieval than content that paraphrases or avoids specific terms. This is a practical argument for terminology consistency and controlled vocabulary in documentation systems that feed into search.
Common Confusion
BM25 is sometimes assumed to be obsolete in AI-powered search, replaced entirely by semantic search and embeddings. In practice, many production retrieval systems use BM25 and semantic search together because they capture different kinds of relevance. BM25 excels at exact term matching; semantic search excels at conceptual similarity. Hybrid systems that combine both often outperform either approach used alone.
Related Terms
Semantic search, embeddings, RAG, reranking, vector database, AI search and retrieval, hybrid search