Building a Production RAG Pipeline with Hybrid Retrieval and LangChain

Most RAG tutorials get you 70% of the way there. This is about the other 30% that actually matters in production.

• Why basic RAG fails
  
  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.




• Hybrid Retrieval fixes this
  
  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.




• Add a reranker
  
  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.




• Measure everything
  
  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.