RAG is a Data Problem Pretending to Be AI

Retrieval-Augmented Generation fails most often not because LLMs “hallucinate,” but because retrieval pipelines return incomplete, stale, or irrelevant context—due to weak chunking, naive ranking, missing metadata/ACLs, and lack of evaluation—so reliable RAG requires treating it like search + ETL with rigorous instrumentation, hybrid retrieval, rerankers, confidence thresholds, and continuous evals.