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Dev.to
7/4/2026
Hybrid Retrieval + RRF: How I Got 100% Retrieval Precision in a Production RAG System

Hybrid Retrieval + RRF: How I Got 100% Retrieval Precision in a Production RAG System

Short summary

Naive RAG systems fail on exact keyword queries and short specific queries, achieving only 72% retrieval precision. The author solved this using hybrid retrieval combining semantic search and BM25 via Reciprocal Rank Fusion algorithm, reaching 100% precision in production with a 600ms latency tradeoff. Includes full Python implementation, evaluation metrics, and tuning guidance for improving RAG answer faithfulness.

  • •Hybrid retrieval using RRF merges semantic and BM25 results mathematically for higher precision
  • •Production results: 72% → 100% retrieval precision, 81% → 87.5% answer faithfulness
  • •600ms latency increase acceptable for most use cases; full code and tuning parameters included

Generated with AI, which can make mistakes.

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