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 production deployments revealed critical failures across legal, API, and support document types. The team replaced fixed 512-token chunking with document-specific strategies — including recursive, semantic, and agentic chunking — achieving recall@10 scores between 91% and 97% depending on content type. They also shifted from pure vector search to a hybrid retrieval system combining vector search, BM25, and cross-encoder reranking, which improved relevance correlation from 0.75 to 0.92. A Bayesian-informed search tuning approach was applied on top of these changes, ultimately reducing end-to-end query latency by 40%. The team published their architecture and Python implementation to guide other engineers facing similar RAG scaling challenges in production environments.