Optimizing RAG at Scale: Chunking, Retrieval, and the Bayesian Search That Cut Latency 40%

Standard RAG implementations often fail in production due to fixed token chunking and high latency. By moving beyond basic semantic search and text-embedding-3-small, a new retrieval layer was built from first principles. This measured, tunable pipeline addresses issues in legal contracts, API docs, and customer tickets. The implementation of Bayesian Search successfully cut latency by 40% and achieved a 95% recall@10, overcoming the limitations of traditional 512-token chunks and slow vector search processes.