What does BM25 do in RAG?

BM25 handles exact keyword matching, retrieving documents based on term frequency and inverse document frequency, which is crucial for specific queries.

Why can't vector search handle everything in RAG?

Vector search can sometimes retrieve semantically similar but contextually irrelevant information, leading to inaccurate answers when precise keywords are needed.

Will I need to understand both BM25 and vector search for RAG?

For building advanced RAG systems, yes. Understanding the strengths and weaknesses of each allows for the creation of more strong and accurate retrieval mechanisms.

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RAG Search: BM25 & Vectors Mix [Analysis]

They promised AI could understand everything. Turns out, it still needs a good old-fashioned index. Hybrid search for RAG isn't a compromise; it's survival.

The AI Catchup Apr 25, 2026 4 min read

Diagram showing two overlapping circles representing BM25 and vector search, with a third overlapping section indicating hybrid search.

⚡ Key Takeaways

Hybrid search combining BM25 (lexical) and vector search (semantic) is essential for effective RAG. 𝕏
BM25 excels at precise keyword matches, while vector search understands meaning and context. 𝕏
Real-world RAG systems need a dynamic approach to choose the best retrieval strategy per query. 𝕏