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 512-token chunking and basic vector search proved inadequate in production environments. They identified that fixed-token chunking split legal clauses mid-sentence, while oversized chunks buried relevant signals in API documentation. The team replaced their single-strategy approach with document-type-specific chunking methods — including recursive, semantic, and agentic strategies — achieving recall@10 scores between 91% and 97% across content types. They also implemented a hybrid retrieval system combining vector search, BM25 keyword matching, and cross-encoder reranking, which improved relevance correlation from roughly 0.75 to 0.92. The combined optimizations reduced overall query latency by 40%, bringing response times down from over one second to more manageable levels for production workloads.