The latest articles on DEV Community by Hector Hernandez Cruz (@hector_hernndez_cruz). https://dev.to/hector_hernndez_cruz https://media2.dev.to/dynamic/image/width=90,height=90,fit=cover,gravity=auto,format=auto/https:%2F%2Fdev-to-uploads.s3.us-east-2.amazonaws.com%2Fuploads%2Fuser%2Fprofile_image%2F4010186%2Fabed1247-2a5b-4fa8-a386-e405228fd872.png https://dev.to/hector_hernndez_cruz en Hector Hernandez Cruz Wed, 01 Jul 2026 00:34:16 +0000 https://dev.to/hector_hernndez_cruz/building-a-production-rag-pipeline-with-hybrid-retrieval-and-langchain-4cdm https://dev.to/hector_hernndez_cruz/building-a-production-rag-pipeline-with-hybrid-retrieval-and-langchain-4cdm <p>Most RAG tutorials get you 70% of the way there. This is about the other 30% that actually matters in production.</p> <ul> <li><p>Why basic RAG fails<br> Embed your docs, retrieve the top-k, pass to the LLM. Simple. But in production you quickly hit a wall. Dense vector search misses exact keyword matches. Keyword search misses semantic meaning. Your retrieval quality plateaus and your LLM starts hallucinating because the wrong context is coming in.</p></li> <li><p>Hybrid Retrieval fixes this<br> Combine dense vector search with BM25 keyword search, then fuse the ranked results using Reciprocal Rank Fusion. You get the best of both worlds and retrieval precision jumps noticeably.</p></li> <li><p>Add a reranker<br> After retrieval, run a cross-encoder reranker on your top candidates. It's slower than embedding similarity but far more accurate. This is the highest ROI improvement you can make after basic RAG is working.</p></li> <li><p>Measure everything<br> Most people skip evaluation entirely. Build a harness that measures hit rate, MRR, and faithfulness before you change anything. Otherwise you're flying blind every time you swap a model or tweak a prompt.</p></li> </ul> ai rag langchain python