The original headline is "How to Catch False 'Done' Claims in AI-Generated Code"

Short summary

AI-generated code often claims to be done before it truly is. The author proposes a structured review loop: state the claim precisely, gather evidence (changed files, test commands, output), check for relevance and consistency, then verdict it as PROVEN, UNPROVEN, or BLOCKED. The approach helps reviewers identify missing evidence and contradictions without re-opening entire tasks.

• •AI code completion claims need evidence-based verification, not trust
• •A four-step loop (Claim, Evidence, Check, Verdict) separates plausible from supported statements
• •Missing or conflicting evidence should yield UNPROVEN status, prompting narrow follow-up

Generated with AI, which can make mistakes.