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Dev.to
Dev.to
7/20/2026
Optimizing RAG at Scale: Chunking, Hybrid Retrieval, and Reranking That Cut Latency 40%

Optimizing RAG at Scale: Chunking, Hybrid Retrieval, and Reranking That Cut Latency 40%

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

Short summary

A detailed walkthrough of rebuilding a production RAG retrieval pipeline with document-type-specific chunking strategies (recursive, semantic, agentic), hybrid retrieval combining vector search and BM25 with Reciprocal Rank Fusion, and cross-encoder reranking. The approach achieved 95% recall@10 and cut latency 40% by parallelizing retrieval stages and funneling 50 candidates to 5 final results. Query transformation expands user queries before search.

  • •Document-type-aware chunking: recursive for legal, semantic for tickets, agentic for wikis
  • •Hybrid retrieval with RRF fusion + cross-encoder rerank achieves 95% recall@10
  • •Query transformation and parallel retrieval cut latency by 40%

Generated with AI, which can make mistakes.

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