Demystifying BM25: The Algorithm That Powers Search

2026-04-09 · Source: Artificial Intelligence in Plain English - Medium · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Software Development & Engineering · Depth: Intermediate, medium

Summary

BM25 (Best Matching 25) is a foundational ranking algorithm used by search engines like Elasticsearch, Lucene, and OpenSearch to determine document relevance to a user's query. It improves upon simpler systems by considering three core factors: Term Frequency (TF), which counts word occurrences with diminishing returns to prevent keyword stuffing; Inverse Document Frequency (IDF), which assigns higher value to rarer words across the database; and Document Length Normalization, which penalizes longer documents and rewards shorter, more concentrated ones. The algorithm includes tunable parameters, k1 (1.2 to 2.0) for term frequency saturation and b (0.75) for document length penalty. Despite the rise of AI, BM25 remains crucial, particularly in Retrieval-Augmented Generation (RAG) for efficiently fetching context for Large Language Models and as a component of Hybrid Search, combining its exact-match strength with vector search's semantic understanding.

Key takeaway

For AI Engineers building search systems, understanding and implementing BM25 is critical. It provides robust exact-match capabilities that complement vector search, especially for specific codes or part numbers. You should consider integrating BM25 into your RAG architectures for efficient context retrieval and leverage it in hybrid search setups to deliver comprehensive and accurate results, ensuring your systems handle both semantic meaning and precise keyword matching effectively.

Key insights

BM25 is a robust keyword-based ranking algorithm crucial for modern search, even alongside AI.

Principles

• Rare words boost scores significantly.
• Repeated keywords have diminishing returns.
• Shorter, relevant documents are rewarded.

Method

BM25 calculates document scores by combining term frequency (with saturation), inverse document frequency, and document length normalization, using tunable parameters k1 and b to customize behavior.

In practice

• Use BM25 for exact-match keyword search.
• Integrate BM25 into RAG systems for context retrieval.
• Combine BM25 with vector search for hybrid results.

Topics

• BM25 Algorithm
• Search Ranking
• Term Frequency
• Inverse Document Frequency
• Document Length Normalization

Best for: AI Engineer, Machine Learning Engineer, Software Engineer

Related on AIssential

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• The State of Information Retrieval in 2026 — Information retrieval has evolved into a reasoning problem, integrating LLMs and agentic search for complex, context-aware query processing.
• Hybrid Search and Re-Ranking in Production RAG — Combining hybrid search, cross-encoder re-ranking, and metadata filtering significantly improves RAG system accuracy for enterprise knowledge.
• Not your usual search system — feat. BM25, FAISS and XGBoost — Production-grade search systems combine lexical and semantic retrieval with learning-to-rank for optimal performance.
• Scaling Retrieval-Augmented Reasoning with Parallel Search and Explicit Merging — MultiSearch uses parallel multi-query retrieval and explicit merging to enhance LLM reasoning and signal-to-noise ratio.
• RAG's Evolution: From Simple Retrieval to Agentic AI — Search evolution progresses from keyword matching to semantic understanding and autonomous, adaptive retrieval.

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Editorial summary, takeaway, and curation by AIssential. Original article published by Artificial Intelligence in Plain English - Medium.