LLMs hallucinate. That's not news. What's underdiscussed is how that failure mode behaves in long working sessions: confident reconstruction that looks fluent, cites specifics, and feels right — until three sessions later when something supposed to be true turns out not to be. This is week 5 of an 8-week deep dive on CRAFT for Cowork , a structured working environment for Claude. The QA framework treats AI reliability as a measurable engineering problem. The four gates CRAFT's verification core is a reusable sub-routine — RCP-CWK-024 — that any recipe can call before reporting a result: Gate 1: File-pointability Can the claim be traced to a specific file? Gate 2: Read-vs-reconstructed Was the data read this session, or recalled from memory? Gate 3: Lessons-Learned conflict Does the claim contradict a documented LL entry? Gate 4: Untested assumption Is this verified or assumed? Enter fullscreen mode Exit fullscreen mode A claim that fails any gate gets flagged — visibly to the user, not buried in the answer.…