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1Clear out junk files and repair common Windows errors2Scan for outdated or missing drivers - takes under a minute3Repair Windows errors before they cause bigger problemsCheck the claims, not the confidence of the answer. Break an AI response into facts you can verify, inspect its citations, compare important points with reliable evidence, and look for missing qualifications. The more harm an error could cause, the more verification—and qualified human review—you should require.
1. Separate the answer into checkable claims
Start by identifying the specific statements the answer depends on. Separate factual claims from opinions, recommendations, and expressions of uncertainty. Prioritize claims that could change your decision, especially figures, dates, current rules, and unusually precise details.
This approach matches the starting point in NIST’s framework for evaluating machine-generated reports: define the information need, then assess whether the report includes the information needed to meet it. NIST’s 2024 report-evaluation publication also describes checking how claims map to their source documents.
2. Open citations and verify the exact claim
A citation is a lead to evidence, not proof that the answer is correct. Open the linked source and find the passage relevant to the claim. Check that the source exists, says what the AI answer attributes to it, and covers the same subject and scope.
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- Faithfulness: Does the cited material support the wording of the claim?
- Completeness: Does the answer preserve the source’s important qualifications and overall message?
- Sufficiency: Is the cited evidence strong enough to support the claim being made?
NIST’s Generative AI Risk Management Framework profile recommends ground-truth comparison and documented fact-checking, particularly when outputs draw on multiple or unknown sources. A NIST project created May 1, 2026, and updated May 5, 2026, describes the three citation checks above as separate evaluation probes: Building Evaluation Probes into Agentic AI.
3. Compare important claims with dependable evidence
When possible, go to the original evidence. Primary sources are especially useful for claims about laws, official procedures, research findings, product specifications, and what an organization says about its own policies. If the primary document is difficult to interpret, compare it with independent, credible sources and note any disagreement rather than choosing the answer that sounds most certain.
NIST’s guidance emphasizes comparison with known ground truth and keeping a record of fact-checking. For a practical check, note which claim you verified, which source supports it, and whether another reliable source reaches a different conclusion. Do not treat repeated wording across sites as independent confirmation if they all rely on the same original source.
4. Check dates, scope, and what a summary leaves out
A statement can be accurate in one context but misleading in another. Look for dates, definitions, geographic limits, populations, exceptions, and other qualifications. Current rules, prices, policies, and schedules need current sources; there is no single recency threshold that works for every topic.
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For an AI summary, compare it with the full source. Check whether it preserves the main point and any limitations or counterarguments that matter to your use. A summary can contain true statements yet still give a distorted impression by leaving out a relevant exception or qualification. NIST’s 2024 report-evaluation work addresses accuracy, completeness, and verifiability; its 2026 evaluation-probe project separately examines whether citations support claims, whether the text preserves the source’s message, and whether the evidence is adequate.
5. Match verification to the consequences of error
Use the potential harm of being wrong to decide how far to check. A quick source check may be enough for a low-impact question. If evidence is uncertain or the claim matters, add independent corroboration. For consequential health, legal, financial, safety, or employment decisions, consult authoritative sources and an appropriately qualified person instead of treating an AI response as the decision authority.
This escalation is a practical application of NIST’s recommendations for human oversight and documented verification, not a replacement for domain-specific professional advice. The more specialist judgment a decision requires, the less appropriate it is to rely on an answer alone.
6. Keep uncertainty visible
If a source is unavailable, outdated, conflicting, or too weak to support a claim, do not silently treat the claim as settled. Record what you could verify, what remains unclear, and what evidence would resolve the question. That distinction helps prevent a plausible answer from becoming a fact simply because it was repeated.
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AI detection does not establish factual accuracy
Whether text was written by AI and whether its claims are true are different questions. NIST’s text-evaluation work treats generation and discrimination—identifying generated text—as separate tasks, and its 2025 challenge examines how generated narratives can be persuasive while misleading. A detector score, polished style, or human-sounding wording does not verify a claim. Check the evidence directly. See NIST’s 2024 GenAI pilot study results, published June 25, 2025, and its 2025 NIST GenAI Text Challenge Evaluation Plan, published September 18, 2025.
Further reading
For broader media and information literacy resources, UNESCO’s Media and Information Literacy topic page includes learning materials related to evaluating information in AI and social-media environments. UNESCO’s 2018 handbook on journalism, fake news, and disinformation covers fact-checking and source and visual verification.
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