RAG promised to solve hallucination by grounding AI in trusted sources. In small, stable domains, it delivers. But as knowledge bases grow, a different failure takes over — one that's harder to detect and more expensive to fix.The system retrieves correct, well-cited information and produces confident answers that don't govern the case at hand. The policy is real but expired. The warranty terms are accurate but belong to a different program. The procedure is valid but assumes a region the customer isn't in. Nothing is fabricated. Everything is cited. The answer is still wrong.This is the applicability problem: the gap between what's relevant and what's allowed to be true for a given situation. Standard RAG architectures have no representation of this gap, and standard evaluation metrics don't measure it.This series names the problem, builds a framework for reasoning about it, and works through what it actually takes to make retrieval systems that know which answer governs before they start generating.