How to Verify AI Code Completion Claims Before Accepting Them

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AI-generated code is often reported as complete before it truly meets requirements, with phrases like 'tests passed' serving as claims rather than verified outcomes. Developers are advised to demand concrete evidence — including changed-file lists, exact test commands, and relevant output — before accepting any completion report. A structured review loop helps categorise each claim as PROVEN, UNPROVEN, or BLOCKED based on whether the supporting evidence is current, relevant, and consistent. Missing evidence, such as a browser-flow fix backed only by a unit test, should move a verdict away from PROVEN until the gap is filled. The approach is not about distrust but about distinguishing plausible statements from properly supported ones, making follow-up actions clearer and more targeted.

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