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Dev.to
7/21/2026
The original title is: "Production RAG optimization: chunking strategies, hybrid retrieval, and reranking that cut latency 40%"

The original title is: "Production RAG optimization: chunking strategies, 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 by document type, hybrid vector+BM25 retrieval with reciprocal rank fusion, and cross-encoder reranking. The author reports achieving 95% recall@10 and 40% latency reduction by moving beyond naive semantic search. Includes production config tables and Python code for each pipeline stage.

  • •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 vector or keyword search
  • •Cross-encoder reranking costs 50ms but gains 15% recall over bi-encoder approaches

Generated with AI, which can make mistakes.

#ai-tools#ai-agents#open-source
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The original title is: "Optimizing RAG at Scale: Chunking, Hybrid Retrieval, and Reranking That Cut Latency 40%"
Dev.to
Optimizing RAG at Scale: Chunking, Hybrid Retrieval, and Reranking That Cut Latency 40%
Dev.to
Optimizing RAG at Scale: Chunking, Hybrid Retrieval, and Reranking That Cut Latency 40%
Dev.to
The original title is: "Optimizing RAG at Scale: Chunking, Retrieval, and Hybrid Search That Cut Latency 40%"
Dev.to