Engineering team cuts RAG pipeline latency 40% using Bayesian search and hybrid retrieval

A development team rebuilt their retrieval-augmented generation (RAG) pipeline from the ground up after standard production deployments proved inadequate for legal, API, and support-ticket use cases. 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% across content types. They combined vector search with BM25 and cross-encoder reranking using Reciprocal Rank Fusion, which improved relevance correlation from roughly 0.75 to 0.92. A Bayesian hyperparameter search was used to tune retrieval weights, ultimately cutting end-to-end query latency by 40%. The approach demonstrates that measurable, tunable retrieval pipelines significantly outperform default RAG configurations in real-world production environments.