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 fixed-token chunking and basic vector search proved inadequate in production environments. The team replaced one-size-fits-all 512-token chunking with document-specific strategies, including recursive, semantic, and agentic chunking, achieving recall@10 scores between 91% and 97% across different content types. They also adopted a hybrid retrieval approach combining vector search and BM25 keyword search via Reciprocal Rank Fusion, followed by a cross-encoder reranking step that improved relevance correlation from 0.75 to 0.92. The 50-to-5 reranking funnel added only 50 milliseconds of latency while delivering a 15% gain in recall. The optimized pipeline ultimately reduced overall query latency by 40% compared to the original setup.