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

Short summary

A detailed engineering breakdown of rebuilding a RAG retrieval pipeline from first principles, covering chunking strategies (recursive, semantic, agentic), hybrid retrieval combining vector search and BM25 with reciprocal rank fusion, and cross-encoder reranking. The article includes production config tables with recall@10 metrics by document type and code for each pipeline stage. Claims a 40% latency reduction and 95% recall@10.

• •Production RAG pipeline with document-type-specific chunking strategies achieving 94-97% recall@10
• •Hybrid retrieval combining vector search, BM25, and cross-encoder reranking via reciprocal rank fusion
• •Includes concrete code, config tables, and latency benchmarks from real deployment

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