Combine dense embeddings and sparse keyword search using Reciprocal Rank Fusion to eliminate retrieval failure modes in production RAG systems. The Bottleneck in Production Most production RAG pipelines start with pure vector search. It works reliably during initial demos, but fails quietly once users begin searching for exact product IDs, error codes, or domain-specific identifiers. Vector search translates text into semantic coordinate spaces. Because dense models optimize ...