BM25 (Best Match 25) is a classical information retrieval algorithm that ranks documents by how relevant they are to a search query — used by traditional search engines, databases, and as a retrieval component in some AI search pipelines.
How BM25 works
BM25 scores documents based on:
- Term frequency: How often query terms appear in the document (with diminishing returns — the 10th occurrence matters less than the 1st)
- Inverse document frequency: How rare the term is across all documents (rarer terms are weighted higher)
- Document length normalization: Penalizes very long documents to prevent them from dominating just through sheer volume
The result: documents that use query terms specifically and don’t just mention them coincidentally rank higher.
BM25 in AI search systems
Many AI search and RAG pipelines use BM25 as a first-pass retrieval stage — fast, interpretable, and effective for keyword-matching — before passing results to a more computationally expensive semantic re-ranker.
Hybrid retrieval: Modern RAG systems often combine BM25 (keyword matching) with embedding-based semantic search (meaning matching) for better coverage. BM25 catches exact keyword matches; semantic search catches conceptually related content that doesn’t share keywords.
Practical implications for content
Understanding BM25 explains why keyword presence still matters in AI-indexed content — not as much as in traditional SEO, but as one of the signals in a retrieval pipeline. Content that uses the specific terminology your target audience uses will score better in BM25-based first-pass retrieval, increasing the chance your page makes it into the semantic re-ranking step.
This is the technical grounding for the advice “use the language your audience uses” — BM25 rewards vocabulary match.