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If an AI answer may be wrong, do not argue with it or silently edit it. Preserve the original response, split it into individual claims, rank those claims by potential harm and how quickly they can change, and verify the important ones against primary or authoritative sources. Check that each source supports the exact wording, date, population, unit and exceptions. For medical, legal, financial, safety, security or reputational decisions, have a qualified human review the result before you act.
Fluent prose, a confident tone, a citation-shaped link or a model’s confidence score is not evidence. Treat the response as a draft containing hypotheses until the underlying claims have been checked.
Why an AI answer can sound right and still be wrong
AI systems can produce incorrect definitions, dates and facts, as well as fabricated quotations, studies, citations and references to sources that do not exist. They may state an error with the same polished certainty used for a correct answer. This makes plausibility a poor test: a sentence can be grammatical, specific and completely unsupported.
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Step 1: Freeze the evidence before checking it
Save the exact prompt and complete answer before asking follow-up questions. Record the model or product name if it is shown, the date and time, the conversation or session identifier, and every link, quotation or file the answer used. Take a screenshot or export the conversation if the interface allows it.
Do not correct spelling, remove hedges or paste a paraphrase into your notes. Those edits can hide the wording you need to test. If the answer changes after a follow-up prompt, retain both versions and label which one made each claim.
- Original input: the prompt, attached documents and relevant settings.
- Original output: the answer exactly as delivered.
- Context: date, jurisdiction, product version, audience and intended decision.
- Evidence references: links, document titles, quoted passages and page numbers.
Step 2: Break the response into atomic claims
A sentence often contains several facts that need different evidence. Split compound statements into units that can each be marked verified, contradicted, unsupported or incomplete. Separate factual assertions from opinions, predictions and recommendations.
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Example of claim extraction
Suppose an answer says, “The 2026 rule requires every employer to retain these records for seven years, and the regulator fines violations up to $50,000.” Create separate checks for the rule’s effective date, the employers covered, the records included, the retention period, the maximum fine and whether the fine is per violation. A source that confirms the rule exists may not establish any of those details.
- Dates, thresholds, prices, dosage instructions and technical specifications.
- Quoted words and attributed statements.
- Causal claims such as “X prevents Y” or “this update fixes the defect.”
- Recommendations that could cause a person to spend money, take a treatment or change a security setting.
Step 3: Rank claims by harm and rate of change
Check the claims that could do the most damage, or become obsolete fastest, first. A current legal requirement, election result, medication dose, account-recovery step or product specification deserves a dated check before a low-impact background definition.
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| Priority | Typical claim | First action |
|---|---|---|
| Critical | Medical, legal, safety, security, financial or reputational decision | Pause action and seek an authoritative source plus qualified human review |
| High | Current rule, price, deadline, eligibility condition, election result or software behavior | Check the latest primary record and its effective date |
| Medium | Technical explanation, historical date or performance statement | Compare documentation, standards or original research |
| Low | Non-consequential definition or wording suggestion | Verify when practical; do not let it delay higher-risk checks |
Priority is about consequences, not how certain the model sounds. A confident sentence about a low-impact fact can wait; a tentative sentence about a dangerous action cannot.
Step 4: Find the best available source
Start with the record closest to the fact. For a law or regulation, use the enacted text and the responsible regulator. For a statistic, use the official statistical agency and its methodology. For a court outcome, use the judgment or docket. For a software feature, use the publisher’s current documentation or release notes. For a scientific claim, look for the peer-reviewed paper and any corrections.
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| Claim type | Preferred evidence | Common weaker substitute |
|---|---|---|
| Legal obligation | Effective statute, regulation, court order or regulator notice | Blog summary without jurisdiction or date |
| Number or trend | Official dataset with definitions and methodology | Infographic with no denominator |
| Scientific effect | Peer-reviewed study, systematic review or scientific body | Press release that omits limitations |
| Product behavior | Current first-party manual, changelog or support article | Old forum post or search snippet |
| Historical event | Archive, contemporaneous record or reputable reference work | Unattributed listicle |
Step 5: Test whether the source actually entails the sentence
Relevance is not proof. Read the passage, heading, footnotes, methodology and limitations, then compare it with the AI’s exact wording. Ask six questions:
- Does the source state the same proposition, or merely discuss the same topic?
- Does it cover the same people, sample, product edition or population?
- Does it use the same unit, denominator and time period?
- Does the jurisdiction match the one in the answer?
- Is the statement final, or is it proposed, estimated, conditional or under review?
- Does the source support the strength of the language, such as “may,” “associated with” or “proves”?
NIST describes validation as confirmation through objective evidence that requirements for a specific intended use have been fulfilled. Apply that principle at claim level: evidence must fit what you intend to do with the answer, not merely resemble its subject.
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Step 6: Check dates, scope, exceptions and numbers
Write down the source’s publication or update date and the date the claim is supposed to describe. Distinguish a rule’s announcement date from its effective date and a proposed requirement from one currently in force. Look for superseded versions, geographic limits, eligibility conditions, exemptions and transitional periods.
For every number, verify its unit and population. “A 20% reduction” could mean relative or absolute change; “$50,000” could be a maximum per case rather than a standard penalty. Confirm whether a figure is a one-time allowance, a recurring amount, a laboratory measurement or a promotional price. Preserve uncertainty intervals and estimates instead of converting them into a single precise value.
Step 7: Independently confirm consequential claims
Use a second authoritative or genuinely independent source when a claim could change a decision, when the first source is secondary, or when the evidence is contested. Two pages that copy the same press release are not independent confirmation.
When sources disagree, compare their dates, definitions and jurisdiction before choosing one. Prefer the primary record for what happened or what a rule says. State the disagreement and the dates checked rather than hiding it behind a single confident sentence. If no source resolves the conflict, mark the claim unresolved and avoid acting on it when the stakes are high.
Step 8: Correct precisely
Label each extracted claim as verified, contradicted, unsupported or incomplete. Then rewrite only the part supported by evidence.
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- Verified: the source directly supports the claim with matching scope and date.
- Contradicted: an authoritative record says the opposite or gives a materially different value.
- Unsupported: the cited material is missing, inaccessible or does not establish the statement.
- Incomplete: the core idea is right but omits a condition, exception, uncertainty or limitation.
Do not replace an unsupported number with a guess. A transparent correction can say, “The official notice confirms the rule takes effect on 1 July, but it does not state the seven-year period claimed in the answer.” That is more useful than preserving a precise-looking error.
Step 9: Keep an audit trail
Store the original answer, your atomic claim list, source links or document identifiers, relevant quotations, dates checked, each decision and unresolved questions. Include enough context for another person to reproduce the check. For a team, record who reviewed each high-risk claim and which version of a document was used.
An audit trail prevents circular checking: you can see whether a later summary came from the same unverified sentence, and you can update a time-sensitive conclusion when the governing source changes.
Step 10: Escalate high-stakes decisions
Ask a qualified professional to review before acting when an error could cause injury, legal exposure, financial loss, security harm, discrimination or a major public-facing mistake. Give the reviewer the original response, your claim list and the evidence you found. An AI system can help organize questions, but it cannot replace the person accountable for the decision.
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How to tell whether an AI citation is real
Open the cited page rather than trusting the title or URL text. Confirm that the page exists, is published by the named organization and contains the quoted passage. Check that the passage has not been taken from a different section, date or jurisdiction. Search the document for the exact number or phrase and read the surrounding caveats.
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A fabricated citation may use a real author’s name with a nonexistent title, a plausible journal volume, a broken identifier or a real source that never makes the claimed statement. If you cannot locate the document or passage, classify the claim as unsupported and continue with a fresh search for the primary record.
Can an AI detector or confidence score tell you the answer is true?
No. AI detectors address whether text may have been generated or assisted by a model; they do not establish that an individual claim is accurate. A detector can misclassify human writing and still say nothing about the evidence. Likewise, a model’s confidence or probability reflects its generation behavior, not the truth of the proposition.
NIST evaluation work emphasizes testing and misleading-content risks, while its pilot materials stress that human-led fact-checking and verification remain crucial. Use a detector or confidence signal only as a prompt to inspect sources, never as a substitute for claim-level verification.
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Worked example: checking a changing product specification
Imagine an AI answer says a device supports a feature, receives an update on a particular date and requires a specific subscription. Extract those as three claims. First check the current manufacturer documentation or release notes for the model and region. Confirm the exact hardware revision, software version and rollout status. Then check the subscription terms for the same date and market. If the documentation confirms the feature but not the date, mark the date unsupported rather than carrying it forward. If a retailer page conflicts with the manufacturer’s manual, prefer the current first-party record and note the discrepancy.
Choosing a checking method
| Method | Authority | Coverage | Speed and effort | Best use |
|---|---|---|---|---|
| Primary-record check | Highest for the specific event, rule or specification | One claim at a time | Moderate effort | Current or high-stakes facts |
| Two-source confirmation | High when sources are independent | Important claims | Higher effort | Consequential or disputed statements |
| Secondary explainer | Useful context, not final proof | Broad overview | Fast | Finding terminology and source leads |
| AI follow-up asking for sources | Uncertain until checked | Can expose more claims | Fast, but requires review | Generating a research checklist |
| Detector or confidence score | Does not measure factual truth | Not applicable to claim verification | Fast | Only a signal to inspect evidence |
Fact-checking checklist
- Save the exact prompt, answer, date, model and links.
- Split every material sentence into testable claims.
- Prioritize by potential harm and speed of change.
- Use the closest primary or authoritative source.
- Match wording, population, unit, jurisdiction and date.
- Read footnotes, methodology, exceptions and uncertainty.
- Confirm consequential claims independently.
- Mark each claim verified, contradicted, unsupported or incomplete.
- Rewrite only what the evidence supports.
- Keep an audit trail and obtain qualified review for high-stakes action.
Bottom line
When an AI answer is wrong, the reliable response is a documented workflow, not a better-sounding prompt. Preserve the evidence, verify claims against the right records, check scope and currency, disclose uncertainty and escalate decisions whose consequences exceed your ability to review them.
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