Why Your RAG System Returns Garbage (And How to Actually Fix It)

So you followed a tutorial, spun up a vector database, embedded some documents, and asked your shiny new RAG system a question. The answer? Completely wrong. Or worse — confidently wrong with citations that don't support the claim. I've been there. Twice in production. Let me walk you through the problems that actually bite you when building RAG systems, and the fixes that got my retrieval quality from "embarrassing demo" to "genuinely useful." The core problem: retrieval is harder than it looks Most RAG tutorials make it seem simple: chunk documents, embed them, do a similarity search, stuff the results into a prompt. That pipeline works great for toy demos with 50 documents about the same topic. It falls apart the moment you have real data. Different document formats, varying levels of detail, ambiguous queries, and chunks that lost their context during splitting — these are the things that actually kill your system. The root cause is almost always the same: your retrieval step retur