Engineering Team Cuts RAG Pipeline Latency 40% With 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 the default 512-token chunking approach with document-type-specific strategies, including recursive, semantic, and agentic chunking, achieving recall@10 rates between 91% and 97% across different content types. They also introduced a hybrid retrieval system combining vector search and BM25 keyword search via Reciprocal Rank Fusion, followed by a cross-encoder reranking stage that improved recall by 15%. A Bayesian search optimization further contributed to cutting overall query latency by 40%, bringing response times down from over one second. The team published their architecture and code patterns to help other engineers move beyond demo-grade RAG implementations toward measurable, production-ready pipelines.