Engineering Team Cuts RAG Pipeline Latency 40% Using Bayesian Search and Hybrid Retrieval

A development team overhauled their Retrieval-Augmented Generation (RAG) pipeline after standard fixed-token chunking and basic vector search proved inadequate in production environments. The team replaced one-size-fits-all 512-token chunking with document-specific strategies, including recursive, semantic, and agentic chunking, achieving recall@10 scores between 91% and 97% across different content types. They also replaced pure vector search with a hybrid retrieval system combining vector search, BM25, and cross-encoder reranking, which improved relevance correlation from roughly 0.75 to 0.92. A Bayesian-informed search approach and parallel retrieval architecture helped reduce overall query latency by 40%, bringing response times down from over one second. The team published their methodology and code on DEV Community to help other engineers build more reliable, tunable RAG systems at scale.