Why RAG Pipelines Fail at Production Scale (And What We Fixed)

5 failure modes we hit building 12+ production RAG systems, and the architectural fixes that actually worked. I've spent the last 14 months building production AI systems for fintech, healthcare, and SaaS clients. Of the 12+ RAG pipelines we've shipped, every single one failed in a different way than it did in staging. Not broke. Failed. Silently degraded. Answered confidently and wrong. Retrieved the right document but extracted the wrong passage. Worked at 10 queries per minute and collapsed at 100. Here's what we kept hitting, and what we fixed. 01 Naive chunking destroys retrieval quality The default in most RAG tutorials is fixed-size chunking, splitting every document into 512-token chunks, embedding them, and then done. It works in demos. In production, it silently kills accuracy. The problem : semantic meaning doesn't respect token boundaries. A contract clause that spans 600 tokens gets split in the middle. A medical report with a critical finding in the second half of a parag