#BM25

Articles about BM25 — exploring patterns, best practices, and real-world implementations in production systems.

1 post tagged with bm25. ← All posts

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Pratik Dhanave · ·6 min read

Ranking — TF-IDF and BM25, the Math of Relevance

Finding the documents that contain your words is the easy part; putting the best one first is where search lives or dies. For decades, the answer to "how relevant is this document to this query?" has come from two intuitions made precise: a word matters more in a document that uses it often, and matters less if it's common across all documents. TF-IDF and BM25 turn those intuitions into the scoring functions that rank most of the world's text search — and understanding them demystifies why search returns what it does.

Finding matches is easy; putting the best first is where search lives. Two intuitions — a word matters more when frequent in a document, less when common across all — become TF-IDF and then BM25, the decades-long default (and the keyword score in hybrid RAG). Its lexical ceiling is the vocabulary gap.

#Information Retrieval#BM25#TF-IDF#Ranking#Search#RAG

All posts on this site are written by Pratik Dhanave, an Agentic AI Architect with 7+ years building production distributed systems, multi-agent AI platforms, and cloud-native infrastructure. About the author → Each article includes working code, architecture diagrams, and references to the specific frameworks and standards discussed. Browse all posts or explore related topics using the tag cloud above.