AI Output Verification

Generated outputs can be wrong in different ways: a claim may lack evidence, a calculation may use the wrong units, code may fail tests or an action may never have occurred. Verification chooses a test suited to the property. Source comparison assesses factual support, executable checks assess code or arithmetic behavior, and state inspection assesses actions. A second model can help review content, but it remains another fallible evaluator. Verification differs from editing for clarity or preference: a polished result may still fail the independent check needed to establish that it is usable.

The practitioner decomposes the result into claims or required properties and identifies an appropriate evidence source or validator for each. It checks cited passages, identifiers, calculations and externally visible effects as relevant. Useful artifacts include a verification checklist tied to task requirements, validators and a record of unresolved uncertainty. Automatic checks can screen common failures, while expert review handles cases without reliable automation. The verification method itself is tested with deliberately incorrect outputs so a passing result has an interpretable meaning.

Verification can fail when sources are wrong, validators are incomplete or reviewers share the original assumption. A model's self-assessment is not independent ground truth. Checking every claim can also be expensive, so effort should follow the consequence and available evidence. The important distinction is between a tested property and a broader guarantee: passing schema, citation or code checks establishes only what those checks actually examine, with remaining uncertainty stated clearly.