Scale AI output verification to stakes
For high-stakes AI outputs, adopt a three-tier verification intensity: skim for low-stakes brainstorming, spot-check key claims for medium-stakes communications, and verify every substantive claim for high-stakes published or production content.
When This Fires
- Every time you use AI output for any purpose beyond personal notes
- When deciding how much review time to allocate to AI-generated content
- When training others on responsible AI-assisted workflows
- When the cost of AI-error propagation varies by context
Common Failure Mode
Uniform verification across all stakes: either verifying everything exhaustively (destroying AI's speed advantage for low-stakes tasks) or spot-checking everything casually (risking undetected errors in high-stakes outputs). The stakes determine the verification intensity, not the perceived quality of the AI output.
The Protocol
(1) Before using any AI output, classify the stakes: Low (brainstorming, exploration, personal notes): errors are costless or quickly caught downstream. Medium (internal communications, working drafts, preliminary analysis): errors mislead but are correctable before external impact. High (publications, client deliverables, production deployments, financial decisions): errors produce external harm, reputational damage, or legal liability. (2) Apply the matched verification: Low → skim for general direction. Accept without deep verification. Medium → spot-check the 3-5 most important claims. Verify numbers, key facts, and logical flow. Accept supporting details. High → independently verify every substantive claim. Check facts against primary sources. Validate logic. Test code. The AI output is a draft, not a deliverable. (3) When in doubt about stakes classification → treat as one level higher than your initial assessment.