Three-Prompt Routine to Verify AI Output Accuracy Before Shipping Code or Docs

A developer has shared a structured three-step verification routine designed to catch errors in AI-generated content before it reaches production systems. The workflow consists of a hallucination sweep that labels all factual claims, an adversarial stress-test that builds the case against a single load-bearing claim, and a stakes-based checklist that prioritizes the most damaging potential errors first. The author argues that most verification failures occur in steps two and three, which people typically skip after an instinctive first pass. In a test case, the checklist prompt correctly flagged a false claim about PostgreSQL extension upgrades being automatic as HIGH stakes, identifying it as the claim that could invalidate an entire database migration plan. The prompts are designed to be copy-pasted directly into an AI tool, making the process take minutes rather than a lengthy manual review.