1 minute readReference note
How are factual claims in AI answers verified?
In brief
AI answer accuracy is assessed claim by claim against evidence appropriate to the entity, date, and subject. The review distinguishes factual error, outdated information, unsupported assertion, ambiguous wording, and a reasonable interpretation. A preferred company message is not automatically the truth standard for independent evaluation.
A useful reference record includes current leadership, ownership, offerings, availability, and other facts that affect stakeholder decisions. Each finding should preserve the prompt, answer, relevant quotation or citation, supporting evidence, and explanation of why the difference matters. Omissions need separate judgment because a short answer cannot contain every fact, yet missing a later resolution can substantially alter its meaning.
Severity and recurrence determine response priorities. A wrong founding date and a false criminal allegation should not carry the same weight. Human review is especially important where claims involve legal status, corporate relationships, or nuanced comparisons. Repeated tests reveal persistence under the sampled conditions; they do not certify every possible answer. The result should show both the observed error and the evidentiary basis for classifying it, so another reviewer can understand or challenge the assessment.