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. The team adopted document-type-specific chunking strategies — including recursive, semantic, and LLM-guided agentic chunking — 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 roughly 0.75 to 0.92. Reciprocal Rank Fusion was used to merge results from parallel retrieval stages without requiring score calibration. The combined optimizations reduced query latency by 40% while significantly improving retrieval accuracy at scale.