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Dev.to
7/20/2026
The original title is: "Optimizing RAG at Scale: Chunking, Retrieval, and Hybrid Search That Cut Latency 40%"

The original title is: "Optimizing RAG at Scale: Chunking, Retrieval, and Hybrid Search That Cut Latency 40%"

Original: Optimizing RAG at Scale: Chunking, Retrieval, and the Bayesian Search That Cut Latency 40%

Short summary

A detailed technical walkthrough of rebuilding a production RAG retrieval pipeline from first principles, achieving 95% recall@10 and 40% latency reduction. Covers document-type-specific chunking strategies (recursive, semantic, agentic), hybrid retrieval combining vector search and BM25 with reciprocal rank fusion, and cross-encoder reranking. Includes production config tables and Python code for each component.

  • •Document-type-specific chunking: recursive for legal (94% recall), semantic for tickets (91%), agentic for wikis (97%)
  • •Hybrid retrieval with RRF fusion of vector + BM25, then cross-encoder rerank (50→5) adds 15% recall for 50ms
  • •Query transformation expands user queries before retrieval, improving match quality on poorly-formed questions

Generated with AI, which can make mistakes.

#ai-tools#research-breakthrough
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Dev.to