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Dev.to
7/19/2026
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 engineering walkthrough of rebuilding a RAG retrieval layer for production, covering chunking strategies (recursive, semantic, agentic), hybrid retrieval combining vector search with BM25 via Reciprocal Rank Fusion, and cross-encoder reranking. The author reports achieving 95% recall@10 and 40% latency reduction by tuning chunk size and overlap per document type and adding query transformation. Includes production config tables and Python code for each component.

  • •Chunking strategy should vary by document type: recursive for legal, semantic for tickets, agentic for wikis
  • •Hybrid retrieval (vector + BM25 + cross-encoder rerank) beats pure semantic or keyword search
  • •Cross-encoder reranking costs 50ms but gains 15% recall; query transformation improves bad user queries

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Production RAG Optimization: Chunking Strategies, Hybrid Retrieval, and Reranking for 95% Recall@10
Dev.to
Production RAG Optimization: Chunking Strategies, Hybrid Retrieval, and Reranking for 95% Recall@10
Dev.to
The original title is: "Optimizing RAG at Scale: Chunking, Retrieval, and Hybrid Search That Cut Latency 40%"
Dev.to
Optimizing RAG at Scale: Chunking, Hybrid Retrieval, and Reranking That Cut Latency 40%
Dev.to