The Real Reason Your RAG Dies in Production — Your Vector DB Is Full of Garbage

§0 About the Person Writing This Non-engineer. 50 years old. Stay-at-home dad in Hokkaido, Japan. Two kids. Vocational high school graduate. I can't write Python. But I designed an AI memory architecture and have 3,540+ hours of AI dialogue experiment data. I recently published this article: $0 Budget, $52M Problem: How a Stay-at-Home Dad Built an AI Memory System That Rivals VC-Funded Startups That article documents the complete design of what I call the "Alaya-vijñāna System" — a three-layer memory architecture for AI. This article is not a sequel. This article is for you — the person whose RAG is dying in production. You've tuned your chunk sizes. Swapped vector databases. Added reranking. The hallucinations won't stop. The moment you push to production, quality collapses. The reason isn't your engineering skill. The reason is that the data inside your vector DB is garbage. This article dissects the structural causes of RAG failure at academic paper quality, and presents "Distillati