Check the claims, not the confidence of the wording. Break an AI response into verifiable statements, inspect its citations, compare important claims with dependable evidence, and look for missing qualifications. The more harm an error could cause, the more verification—and expert review—you need.
1. Separate the answer into claims you can check
Start with the facts that could change your decision. An AI response may mix factual statements with advice, interpretation, and uncertainty; assess those separately rather than treating the whole response as one claim. Prioritize information that is decision-critical, time-sensitive, numerical, or unusually specific.
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NIST’s proposed framework for evaluating machine-generated reports starts by defining the information need and checking whether the report includes the required information. In practice, write down the question you need answered and the key facts the response must get right. (NIST, July 14, 2024)
2. Follow citations to the evidence
A citation is a lead to inspect, not proof that the answer is correct. Open each relevant link and find the passage the AI appears to rely on. Confirm that the source exists, that the passage supports the exact claim, and that the source is authoritative enough for the question.
#1 Best Overall
- Faithfulness: Does the cited source actually support the claim?
- Completeness: Does the response preserve the source’s full message, including important qualifications?
- Sufficiency: Is the evidence strong enough to justify the claim?
NIST’s report-evaluation work examines how claims map to source documents. Its 2026 evaluation-probe project describes faithfulness, completeness, and sufficiency as distinct citation checks. A real source can still be irrelevant, incomplete, or inadequate for the claim attached to it. (NIST, July 14, 2024; NIST, updated May 5, 2026)
3. Compare important claims with dependable evidence
For claims about laws, official procedures, research findings, product specifications, or an organization’s own statements, look for the primary document or source. If the original is hard to interpret, compare more than one independent, credible source and note any disagreement. Don’t count two pages as independent corroboration if both repeat the same underlying source.
NIST recommends comparison with known ground truth and documented fact-checking, particularly when generated content draws on multiple or unknown sources. Keep a brief record of what you checked and what remains unresolved. (NIST, AI Risk Management Framework: Generative AI Profile, 2024)
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4. Check what the answer may have left out
A response can contain true statements yet still mislead if it drops a condition or counterpoint. This matters especially when an AI summarizes a document: compare the summary with the full source and look for omissions that would change its meaning.
- Time: Is the information current, or could a rule, price, policy, or schedule have changed?
- Scope: Does the claim apply to the relevant location, population, product version, or situation?
- Definitions and exceptions: Are key terms defined, and are important exceptions included?
- Counterevidence: Does the source or another credible source qualify the conclusion?
There is no single recency threshold that works for every claim. A historical explanation may remain useful for years; a current procedure or price may need a very recent source. Check the date and scope against the decision you are making. (NIST, updated May 5, 2026)
5. Match verification to the consequences
Use the likely cost of an error to decide how far to go. A quick source check may be adequate for a low-impact question. When evidence is uncertain or the consequences are greater, add independent corroboration. For a decision requiring specialist judgment, consult an appropriately qualified person rather than treating an AI response as the authority.
| Situation | Practical check |
|---|---|
| Low impact; a clear, relevant source is available | Check the cited passage and its date and scope. |
| Important claim; source is unclear, incomplete, or disputed | Find primary evidence where possible and compare independent credible sources. |
| Health, legal, financial, safety, or employment decision with serious consequences | Verify authoritative evidence and involve an appropriately qualified professional. |
This is a practical application of NIST guidance on human oversight and documented verification, not a substitute for professional advice in a specific field. (NIST, AI Risk Management Framework: Generative AI Profile, 2024)
6. Keep uncertainty visible
If a source is inaccessible, old, conflicting, or too weak to support a claim, don’t silently turn uncertainty into certainty. Note what you verified, what you could not establish, and what additional evidence would resolve the question. That distinction is especially useful when you share or reuse an AI-generated answer.
Best Value
Why AI detection and factual checking are different
Whether text was generated by AI and whether its claims are true are separate questions. A detector score does not verify a fact, and human-sounding prose does not make a claim reliable. NIST’s text-evaluation work treats generation and detection as distinct tasks, and its 2025 challenge examines how generated narratives can be persuasive while misleading. Check the evidence for the claim itself. (NIST, 2024 pilot results, published June 25, 2025; NIST, 2025 challenge evaluation plan, published September 18, 2025)
Quick Recap
Further reading
- UNESCO’s Media and Information Literacy resources, including material on evaluating information in AI and social-media environments.
- UNESCO IITE’s Media and Information Literacy program.
- UNESCO’s 2018 journalism education handbook on fake news and disinformation, with material on fact-checking and source and visual verification.
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