Treat factual claims in an AI-generated answer as unverified until you check them. Break the answer into individual claims, open its citations, compare important points with authoritative evidence, and check that sources are current and relevant. Fluent wording, confident tone, and a list of links do not establish that an answer is true.
Check the answer claim by claim
A long AI response can combine accurate information with errors, outdated details, unsupported interpretations, or invented quotations. Do not decide whether the whole answer is “right” at a glance. Identify the statements that can be checked, especially:
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- Names, dates, quotations, and numerical figures
- Claims about cause and effect
- Legal, medical, financial, or technical interpretations
- Recommendations that depend on facts or assumptions
The UK House of Commons Library recommends identifying the claims before checking them. Its guidance also warns that definitive factual answers, contested issues, and legal or policy interpretation can be difficult areas for AI. Read the House of Commons Library guidance on AI and research.
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Open citations and test what they support
Visit each cited link rather than relying on its label or on the AI’s description of it. Check that the page exists, is from the stated publisher or author, and supports the precise statement attached to it. A real source can still be irrelevant, misrepresented, or too weak to substantiate the claim; a plausible-looking reference may not exist at all.
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OpenAI warns that ChatGPT can produce incorrect facts and fabricated quotations, studies, citations, or references. It advises checking important information against reliable sources and visiting links directly. OpenAI: Does ChatGPT tell the truth?
Prefer evidence close to the original claim
When a claim matters, follow it back to the strongest available evidence rather than stopping at a page that repeats it. Depending on the subject, useful sources may include official statistics, legislation, government departments, regulators, technical documentation, peer-reviewed research, or a relevant expert briefing. The House of Commons Library lists these among examples of reputable sources.
Assess a source by how directly it connects to the evidence, whether the publisher has relevant authority, and whether its scope matches the claim. A government source may be authoritative for its own rules or statistics; it may not settle every question about how those rules should be interpreted.
Check date, scope, and context
Even a once-correct answer can become outdated. Check the source’s publication or update date, the jurisdiction it covers, relevant product or technical version, definitions, and any stated limitations. For fast-changing information, look for current material from the responsible official body. Make sure the AI’s statement and the source are talking about the same place, time period, and meaning of a term.
Look for independent confirmation
For important claims, compare a second authoritative source that is independent of the first. Multiple pages are not meaningful confirmation if they all repeat the same original report or copy one another. Prefer sources with separate evidence or expertise, and investigate disagreements rather than averaging them into a convenient answer.
Escalate consequential questions
For medical, legal, financial, or safety-critical decisions, use the relevant primary authority and consult a qualified professional when appropriate. AI can help you identify questions or locate material, but its response is not a substitute for professional judgment. The Commons Library describes careful checking, ideally with an expert, as the best guard against AI hallucinations.
Keep facts separate from interpretation
As you check, record which claims are confirmed, which are interpretations, and what remains unknown or dependent on missing context. Do not turn a source’s silence or an unresolved disagreement into a confident conclusion. If you cannot establish a claim, leave it unverified rather than repeating it as fact.
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Why confidence and AI-detection clues are not enough
A confident tone is not evidence of correctness. OpenAI puts it plainly: “Confidence isn’t reliability: The model may express high confidence even in incorrect answers.” Search or research features can help surface current sources, but the citations still need to be checked against the exact claims they are meant to support.
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Likewise, vague wording, weak evidence, odd citations, or inconsistent detail can be reasons to scrutinize an answer; they do not prove that it was generated by AI or that it is false. The Commons Library cautions that such indicators are not definitive and that detection tools should not be treated as conclusive. Checking the evidence answers a different question: whether the claims are supported.
How accuracy is assessed for AI systems
Checking one response is different from measuring an AI system’s general accuracy. NIST defines accuracy as the “closeness of results of observations, computations, or estimates to the true values or the values accepted as being true.” Its AI Risk Management Framework says meaningful assessment should use defined, realistic test sets representative of expected use and documented methods. That is system-level evaluation guidance, not a guarantee that any individual answer is correct or a universal percentage for how often a model gets things right. Read the NIST AI Risk Management Framework.
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