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.
Top comments (1)
Your article on building a production RAG pipeline really hits on the challenges of the last 30%. One critical aspect you might want to consider is how to ensure document provenance and verification in your pipeline. For instance, using DocImprint's MCP server can help you extract and summarize content while generating evidence bundles that include SHA-256 hashes. This not only proves the integrity of the captured data but also allows for offline verification, which could be a game changer for compliance in AI applications. You can explore this further at docimprint.com/mcp.