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 production deployments revealed critical failures across legal, API, and support document types. The team replaced fixed 512-token chunking with document-specific strategies — including recursive, semantic, and agentic chunking — achieving recall@10 scores between 91% and 97% depending on content type. They combined vector search with BM25 and cross-encoder reranking via Reciprocal Rank Fusion, reducing latency by 40% while improving retrieval precision. A cross-encoder reranking stage, which narrows 50 candidate results down to 5, added only 50ms of overhead but delivered a 15% gain in recall. The rebuilt pipeline moved the system from an ad-hoc demo setup to a measurable, tunable retrieval architecture suited for large-scale production use.