I Switched From Pure Vector Search to Hybrid Retrieval in My RAG System — Here's What Changed

I've been building RAG (Retrieval-Augmented Generation) systems for a while now, and I recently made one change that boosted my retrieval accuracy from ~60% to ~85%. The change? Adding BM25 keyword matching alongside my existing vector search. That's it. No fancy model swaps. No expensive rerankers. Just combining two search strategies that complement each other's blind spots. Let me walk you through exactly what happened, why it works, and what I learned from other engineers running RAG in production. The Problem With Pure Vector Search Vector search (using embeddings + cosine similarity) is incredible at understanding meaning . Ask it for "employee vacation policy" and it'll find documents about "time off benefits," "annual leave," and "PTO guidelines." But here's the catch — it sometimes misses exact terminology . In my test set of 50 questions against internal documentation, I kept running into this pattern: User query: "What's the PTO policy?" Vector search found: Chunks about "va