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

A development team rebuilt their Retrieval-Augmented Generation (RAG) pipeline from scratch after standard 512-token chunking and basic vector search proved inadequate in production environments. The team adopted document-type-specific chunking strategies — including recursive, semantic, and agentic methods — achieving recall@10 scores between 91% and 97% across legal, API, and support content. They replaced pure vector search with 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 approach and parallel async retrieval together reduced end-to-end query latency by 40%, bringing it well below the original one-second threshold. The team published their architecture and code configurations as a practical guide for engineers scaling RAG systems beyond the demo stage.