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Fact-check an AI answer claim by claim, not impression by impression. Separate factual statements from interpretations and predictions, open every important citation, check that the source exists and actually supports the wording, then corroborate consequential claims with an independent authoritative source. Record the evidence, date, version, jurisdiction, and remaining uncertainty so another person can reproduce your decision.

What fact-checking an AI answer actually involves

A fluent answer is not a validated answer. NIST distinguishes validation—confirmation through objective evidence that requirements for a specific intended use have been met—from accuracy, the closeness of a result to values accepted as true. Applied to an AI response, that means plausibility, confidence, and polished prose are not evidence. You must test whether each material claim is true enough, current enough, and applicable enough for the decision you will make.

The practical unit of checking is the individual claim. One paragraph can contain a correct date, an unsupported causal explanation, a calculation, and a prediction. A single citation at the end does not validate all four. The House of Commons Library recommends precisely identifying each fact or claim in generated material and verifying each with an independent source. Use that principle as the backbone of your review.

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Begin with a claim inventory

Copy the AI response into a working document and split it into statements that could be checked separately. Preserve qualifiers such as “in the United States,” “as of 2024,” “for adults,” or “in the cited study.” Removing those words can turn a supported statement into a false one.

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Classify every statement

  • Fact: A statement about an event, person, number, rule, specification, or condition that can be checked against evidence.
  • Interpretation: An explanation of what evidence means. It may be reasonable without being directly stated by a source.
  • Calculation: A result that should be reproducible from stated inputs and a method.
  • Prediction: A forecast about what will happen; it cannot be verified in the same way as a past event.
  • Opinion or recommendation: A value judgment or proposed action, which should be presented as such rather than as a fact.

Mark which claims are central to the conclusion, surprising, time-sensitive, or consequential. Check those first. A minor descriptive error may not change the decision; a wrong dosage, legal deadline, security instruction, or financial assumption can.

Build a priority queue

  1. Highlight claims whose failure would change the answer’s recommendation or outcome.
  2. Next, flag claims involving safety, health, law, money, privacy, public policy, or another person’s rights.
  3. Then mark numbers, quotations, dates, version-specific behavior, and statements that depend on geography or population.
  4. Leave low-impact background wording until the material claims are resolved.

Audit the citation before trusting it

An AI-generated citation can be real but irrelevant, or completely invented. Check it in a fixed sequence rather than assuming that a recognizable title or a working link proves anything.

1. Existence

Open the cited article, report, court decision, dataset, standard, or documentation page. A dead link, nonexistent paper, invented DOI, or search result with no underlying document is a verification failure. Do not use a search-result snippet as evidence; it can be truncated, stale, or assembled from text that does not appear in the source.

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2. Identity

Compare the document’s title, author or issuing organization, publication date, edition, DOI where applicable, and URL with the citation. Check that an AI has not combined the title of one document with the author or date of another. For a web page, note whether the page has been updated and whether the cited version still exists.

3. Entailment

Locate the passage, table, figure, or section that supposedly supports the claim. Ask whether it supports the exact sentence, including its population, geography, timeframe, magnitude, and conditions. A source that says a treatment was studied in 120 adults does not support a claim about children; a technical manual for version 3 does not establish behavior in version 4.

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4. Context

Read before and after the quoted material. Look for definitions, exclusions, confidence intervals, study limitations, legal exceptions, and statements that the result is preliminary. Check whether the AI omitted a contrary finding or converted an association into causation. If the response uses quotation marks, confirm the words exactly and record who said them, that person’s role, the document, and the page or section.

5. Authority

Judge whether the source is fit for the claim. Original legislation, court and regulator documents are appropriate for legal questions. Government statistical agencies are preferable for official figures. Standards bodies and first-party technical documentation are stronger for specifications. For research findings, trace a summary to the original peer-reviewed paper. A secondary explainer can help you find evidence, but it should not be the final support for a high-consequence assertion when a primary source is available.

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6. Independence

Find a separate authoritative source for material claims. Ten pages that copy the same press release are one evidentiary chain, not ten independent confirmations. Independence is especially important when the original source is a company, advocacy group, or party with an interest in the outcome.

7. Record

Save the relevant passage or table, its page or section, the access date, the document version, and your judgment. A record turns a one-time check into an auditable decision and lets someone else repeat it after the source changes.

Use a source hierarchy that matches the question

There is no universal “best” source. The right evidence depends on what you are trying to establish.

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Claim type Preferred evidence What to verify
Law, regulation, court outcome Current legislation, court opinion, regulator notice, or official guidance Jurisdiction, effective date, exceptions, and whether the text is current
Population or economic figure Government statistical agency or original dataset Definition, sampling frame, geography, period, revisions, and units
Technical specification Standards body or first-party documentation Product or protocol version, supported configuration, and documented limitations
Scientific finding Original peer-reviewed paper, trial record, or systematic review Methods, population, comparator, uncertainty, conflicts, and whether the result is causal
Company capability or performance First-party documentation plus independent testing where available Edition, test conditions, date, scope, and marketing qualifications
Historical event Archival record, contemporaneous document, or reputable scholarly source Provenance, date, translation, and whether later commentary is being mistaken for primary evidence

The FTC’s information-quality guidance describes reliable information as accurate, clear, complete, unbiased, contextualized, and accompanied by source identification and transparent documentation. Use those properties as a quality test rather than treating the publisher’s reputation as a substitute for reading the evidence.

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Compare competing answers or sources systematically

When two AI answers disagree, do not choose the one that sounds more certain. Put their claims and evidence side by side and score the dimensions that matter for your use.

Comparison axis Questions to ask
Authority Who created the evidence, and are they responsible for the underlying record?
Directness Does the source state or measure the claim, or merely repeat someone else?
Date and version Which source reflects the relevant time, release, policy, or edition?
Jurisdiction and population Does it apply to the country, users, subjects, or setting in question?
Methodological quality How were data collected, compared, analyzed, and checked?
Transparency Are methods, data, assumptions, limitations, and conflicts disclosed?
Corroboration Is there genuinely independent confirmation?
Consequence of error How much evidence and review are justified if the claim is wrong?

If authoritative sources conflict, show the conflict instead of manufacturing a single verdict. Explain whether the difference comes from a newer date, a different definition, jurisdiction, population, or method. If the scope cannot be reconciled, label the claim unresolved and describe what additional evidence would settle it.

Handle time, versions, and changing web pages

Dynamic claims need a timestamp. Record when you accessed the page and the version or edition you used. A current price, software behavior, eligibility rule, threat description, or public-health recommendation can change after an answer was generated. “The documentation says” is incomplete unless the relevant release or date is clear.

For a web page that changes without an obvious version, save a local copy or a permitted excerpt, note the page’s displayed update date, and capture the exact heading or paragraph. For legislation and policy, check the effective date rather than relying on the date the document was uploaded. For statistics, look for revisions and release notes; a later revision can legitimately replace an earlier figure.

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Keep an evidence ledger

A compact ledger prevents a long answer from becoming an untraceable collection of browser tabs. Use one row per claim.

Field What to enter
Claim ID A short label such as C-07
Exact claim The AI’s wording, including qualifiers
Claim type Fact, interpretation, calculation, prediction, or opinion
Importance Central, consequential, time-sensitive, or background
Source identity Title, author or organization, date, edition, and URL
Supporting location Page, section, table, figure, or quoted passage
Scope Jurisdiction, population, timeframe, version, and conditions
Independent check The second authoritative source and what it confirms
Judgment Verified, partly supported, unsupported, contradicted, or unresolved
Notes Limitations, conflicts, access date, and follow-up needed

Use “partly supported” when only some of a compound sentence is evidenced. Use “unsupported” when the source is real but does not establish the claim. Use “contradicted” when reliable evidence points the other way. “Unresolved” is the honest result when evidence is incomplete or authoritative sources remain in conflict.

What to do with calculations, quotations, and predictions

Recalculate numerical claims

Copy the inputs and formula into a calculator or spreadsheet. Check units, rounding, denominators, date ranges, and whether the AI mixed percentages with percentage points. If the source gives a range or uncertainty interval, preserve it; replacing a range with a single precise number creates false confidence. A calculation is verified only when another person can reproduce it from the recorded inputs.

Verify quotations word for word

Search the original document, not a quote-collection page. Confirm the speaker, role, event or publication, date, and location in the source. Ellipses can remove a qualification, and translations can change wording. If the exact words cannot be found, remove quotation marks and describe the idea only if the underlying source supports it.

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Treat predictions as forecasts

A forecast cannot be proven true at the moment it is made. Check the model, assumptions, historical comparison, uncertainty, and time horizon. Label it as a prediction and avoid presenting a probability as a guarantee. When the forecast affects a decision, define what outcome would count as success and when you will reassess it.

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Use AI as an assistant, never as the final reviewer

AI can help extract candidate claims, suggest search terms, organize a ledger, or compare passages you provide. It cannot decide whether a source is authoritative or applicable without human judgment. Ask it to show the exact sentence and limitation from a document, then open the document yourself.

Do not treat an AI detector as a fact-checker. Detecting whether prose was AI-generated says nothing about whether its claims are true. Likewise, a citation’s presence is not evidence of accuracy. CDC guidance states that AI tools cannot be authors or coauthors and should not be cited as authors or primary sources. Cite the underlying human-authored or institutional source instead.

NIST’s work on synthetic-content transparency describes provenance, authentication, labeling, detection, testing, auditing, and maintenance as complementary approaches. Provenance metadata can show where a file came from or how it was handled, but it does not replace reading the source or validating the claim.

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Raise the threshold for high-stakes decisions

For medical, legal, financial, safety, public-policy, employment, or identity decisions, do not stop at a plausible source match. Obtain current primary evidence, check the relevant jurisdiction or patient population, and ask a qualified professional to review the conclusion when the consequences justify it. An AI answer may help you formulate questions, but it is not a substitute for professional judgment or an official determination.

  • Medical: verify the indication, dose, contraindications, patient group, and publication date with a licensed clinician and authoritative medical guidance.
  • Legal: confirm the controlling jurisdiction, effective date, procedural posture, and exceptions with current legislation, case law, or a qualified lawyer.
  • Financial: check assumptions, fees, tax treatment, risk, and time horizon; do not treat a forecast as a promise.
  • Security and safety: test instructions in an isolated environment, consult current vendor or standards documentation, and use a formal change-control process.

A worked example of the workflow

Suppose an AI answer says: “A 2023 study proves that a particular intervention cuts workplace injuries by 40% nationwide, and a regulator requires every employer to adopt it by July.” Treat that as several claims, not one.

  1. Split it into the study’s year, the intervention, the 40% estimate, the word “proves,” the nationwide scope, and the regulatory deadline.
  2. Classify the estimate as a research finding, “proves” as an interpretation, and the deadline as a legal or regulatory fact.
  3. Open the cited study and check its design, sample, comparison group, confidence interval, setting, and whether it measured injuries or a proxy outcome.
  4. Check whether the study covered the whole country or a limited workplace sample. If it was observational, do not rewrite an association as proof of causation.
  5. Find the regulator’s current notice or rule. Confirm the jurisdiction, covered employers, effective date, exemptions, and whether July is a proposal, compliance date, or comment deadline.
  6. Locate an independent official statistic or review to see whether the 40% estimate is consistent with other evidence.
  7. Record each result separately. The final wording might state that the study observed a reduction in its sampled workplaces, that causation and nationwide generalization are not established, and that the regulator’s rule has a different scope or date.

This method avoids the common error of allowing one genuine citation to lend credibility to unsupported additions in the same sentence.

Common failure modes and their fixes

  • Trusting a polished tone: Require a source and an exact supporting passage for every material fact.
  • Checking only the URL: Verify identity, entailment, context, and scope after the page opens.
  • Using snippets as proof: Read the complete original document.
  • Accepting a source that supports only part of a sentence: Split the sentence and mark unsupported clauses.
  • Confusing repetition with corroboration: Trace sources to independent underlying evidence.
  • Ignoring dates and versions: Record access date, edition, release, and effective date.
  • Filling gaps with a confident inference: State what could not be verified.
  • Letting a detector stand in for review: Assess the truth and evidence of claims directly.
  • Forgetting consequences: Escalate high-stakes claims to current primary sources and qualified review.

Final publication checklist

  1. Have you split the answer into individual, qualified claims?
  2. Did you prioritize central, surprising, time-sensitive, and consequential statements?
  3. For each citation, did you confirm existence, identity, entailment, context, authority, independence, and record?
  4. Are dates, versions, jurisdictions, populations, units, and conditions attached to the claims they qualify?
  5. Did you distinguish facts from interpretations, calculations, predictions, and opinions?
  6. Did you reproduce quotations exactly and identify the speaker and location?
  7. Did you explain conflicts instead of forcing a single verdict?
  8. Are unresolved or partly supported claims labeled plainly?
  9. For high-stakes use, did a suitable professional or official source review the conclusion?
  10. Could another reader reproduce your check from the evidence ledger?

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