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Do not fact-check an AI answer as one block of prose. Split it into individual claims, classify each claim, and verify the important ones against the underlying documents. Open every citation, check that it exists and says what the answer claims, then corroborate consequential points with an independent authoritative source.
This method catches fabricated references, overstated conclusions, stale information, missing qualifiers, and real sources used to support the wrong sentence. It also gives you a record another person can audit.
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What fact-checking an AI answer actually means
Fact-checking is not judging whether a response sounds polished, nor checking whether it contains footnotes. It is a structured comparison between each factual assertion and objective evidence suitable for that assertion.
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NIST distinguishes validation from mere plausibility: “Validation is the confirmation, through the provision of objective evidence, that the requirements for a specific intended use or application have been fulfilled.” Applied to an AI response, a fluent paragraph is not validated simply because it is coherent. You must establish whether its claims are true, applicable to the intended use, and supported by evidence.
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Accuracy is also narrower than truth in the abstract. NIST describes accuracy as closeness to values accepted as true. A number can be accurate for one country and year but wrong for another; a legal rule can be correct in one jurisdiction but inapplicable in another; a product specification can be obsolete after a revision. Your check therefore needs scope, date, version, and intended use.
Start with the claims, not the citations
Mark every sentence that asserts or implies something verifiable. Long sentences often contain several claims joined by “and,” “because,” or “which means.” Separate them before searching. For example, “The regulation took effect in 2024, applies to small businesses, and requires annual audits” is at least three claims with potentially different sources and exceptions.
Classify what the answer is saying
| Claim type | What to check | Typical evidence |
|---|---|---|
| Fact | Whether the event, rule, number, quotation, or specification is true within a stated scope | Official records, datasets, standards, filings, or the original publication |
| Interpretation | Whether the conclusion follows from the cited material and whether competing interpretations exist | Primary text plus reputable analysis that explains the reasoning |
| Calculation | Inputs, formula, units, rounding, and whether the calculation uses the right population or period | Source data and a reproducible calculation |
| Prediction | Assumptions, model, time horizon, uncertainty, and track record where available | Methodology, scenario data, and clearly stated uncertainty |
| Opinion or recommendation | Whose judgment it is, what criteria it uses, and whether it is presented as a value choice rather than a fact | Explicit criteria and evidence relevant to the decision |
Do not mark a prediction “false” merely because it is uncertain, and do not accept an interpretation as a fact just because a source is linked. Record the appropriate status for each claim.
A practical claim-by-claim workflow
- Preserve the original response. Save the exact text, the model or service name if shown, the conversation date, attached files, and any links. A later regenerated answer may differ.
- Extract atomic claims. Give each claim an identifier and copy it without silently correcting wording. Include implied claims, such as a statement that a study “proves” a treatment works.
- Rank the claims. Check first those that are central to the conclusion, surprising, time-sensitive, consequential if wrong, or difficult to reverse. Legal, medical, financial, safety, and public-policy assertions deserve the highest threshold.
- Find the cited source. Open the link rather than trusting the citation label, title, or search-result snippet. If there is no usable link, search by the exact title, authoring organisation, distinctive phrase, DOI, case number, or document number.
- Run the citation audit. Check existence, identity, entailment, context, authority, and independence using the tests below. Save the relevant passage and location.
- Corroborate material claims. Locate a separate authoritative source, preferably one produced independently of the first. Compare definitions, dates, populations, jurisdictions, and methods.
- Assign a status. Use “verified,” “partly supported,” “unsupported,” “contradicted,” or “unresolved.” Explain the reason in one or two precise sentences.
- Report limits. State what the evidence does not establish. If the source is current only through a particular date or edition, say so.
How to audit an AI citation
1. Existence: is there a real source?
Visit the URL and search the publisher’s own catalogue or archive. A dead link, invented DOI, nonexistent case, or citation that resolves to an unrelated page is a verification failure. A search-result snippet is a lead, not evidence; snippets can be truncated, stale, or assembled from text that does not support the answer.
2. Identity: does the reference match?
Compare the title, author or issuing organisation, publication date, edition, document number, DOI, and URL. Watch for a real article with a similar title, a later revision substituted for the cited edition, or a secondary page presented as the original. For a quotation, confirm the speaker, role, date, and location in the document.
3. Entailment: does it support the exact sentence?
Read beyond the highlighted line. Ask whether the source supports every part of the claim, including the number, causal language, population, geography, and timeframe. A paper reporting an association does not automatically support “causes.” A source describing a pilot group does not support a statement about everyone.
4. Context: what was omitted?
Look for definitions, footnotes, eligibility rules, confidence intervals, exclusions, adverse findings, legal exceptions, and the authors’ own limitations. AI systems often compress a qualified finding into an unconditional sentence. A real citation can therefore be misleading even when the link and title are genuine.
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Match source type to claim. Prefer original legislation, court opinions, and regulator documents for legal claims; government statistical agencies for official figures; standards bodies and first-party technical documentation for specifications; original peer-reviewed papers for research findings; and company filings or documentation for company-specific claims. Secondary explainers can help you locate evidence, but trace an important assertion to the underlying source.
6. Independence: is there corroboration?
Find a separate authoritative source rather than several pages copying the same press release or AI-generated text. Independence matters most when the claim affects a decision or when the first source has a financial, political, or institutional stake.
7. Record: can another person reproduce your check?
Save the URL, access date, document version, page or section, relevant quotation or data row, and your judgment. Note any archive link or download filename if the page may change. For dynamic information, record the date and version alongside the claim, not in a general note at the end.
Choosing trustworthy sources
“Authoritative” does not mean “famous.” It means the source has the competence, access, method, and accountability appropriate to the question.
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|---|---|---|
| What does a law or regulation require? | Legislation, court or regulator document, and the applicable jurisdiction | These are the controlling texts; summaries may omit exceptions |
| How large is a population or trend? | Government statistical agency or the dataset owner | Definitions, sampling, revisions, and metadata are available |
| What does a technology standard or product specification say? | Standards body or first-party technical documentation | Version and conformance details are explicit |
| What did a study find? | Original peer-reviewed paper and its supplementary material | Methods, population, analysis, and limitations can be inspected |
| What did an organisation report about itself? | Regulatory filing, audited report, or official documentation | The claim can be tied to a dated, accountable record |
Use a secondary source to understand terminology or discover the primary document, then move upstream. Marketing pages, unsourced blogs, reposts, and anonymous summaries may be useful leads but are weak final evidence for a material claim.
How to check numbers, quotations, and calculations
Numbers need definitions
Copy the source’s definition, unit, denominator, geography, and period. “Unemployment,” “users,” “revenue,” and “accuracy” can each have multiple legitimate definitions. Check whether the figure is preliminary, revised, seasonally adjusted, modelled, or a one-time allowance. Do not import a supposedly universal statistic when no safely established statistic exists for the question.
Calculations need reproducible inputs
Write down every input and formula. Recalculate percentages from the cited table, preserve units, and inspect rounding. If an answer says a value “doubled,” verify the baseline and endpoint; a change from 1 to 2 is 100 percent growth but a two-unit increase. If the source uses a sample, do not silently present the result as a whole-population measurement.
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Quotations need exact wording and context
Search the document for the quoted words. Confirm punctuation where meaning depends on it, identify who spoke or wrote them, and read the surrounding paragraph. If the sentence cannot be found, label the quotation unsupported rather than reconstructing what the source probably meant.
Handling dates, versions, jurisdictions, and changing information
Many apparent AI errors are scope errors. A technically correct statement can be wrong for the reader’s location, edition, or date.
- Date: Record publication date and the date you accessed the information. For live prices, rules, software, and statistics, state the “current as of” date.
- Version: Check the standard, software release, policy edition, dataset revision, or model specification named in the answer. A later page may describe a changed requirement.
- Jurisdiction: Identify the country, state, province, court, regulator, or market. Do not generalise a local rule.
- Population: Match the source’s participants, customers, age range, industry, or eligibility group to the claim.
- Timeframe: Separate an event date from publication date and a forecast period from the date it was made.
When two authoritative sources conflict, do not force a single verdict. Show the conflict, explain whether the difference comes from scope, definitions, methodology, or date, and state which source governs the reader’s use case.
How to grade the result and communicate uncertainty
A useful fact-check is more informative than a binary “true” or “false.” Use a consistent status:
- Verified: The cited evidence directly supports the claim within the stated scope, and important details are corroborated.
- Partly supported: The source supports one component but not the full wording, or an important qualifier is missing.
- Unsupported: No suitable evidence was found, even if the claim sounds plausible.
- Contradicted: A more authoritative or directly applicable source conflicts with the answer.
- Unresolved: Evidence is incomplete, inaccessible, conflicting, or too ambiguous to decide.
Explain the status with the smallest sufficient reason: “The report exists, but its sample covers one city, so the nationwide claim is partly supported.” Do not replace an unresolved gap with a confident inference.
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Human review, AI assistance, and provenance
AI can speed up the mechanical parts of checking. It can extract candidate claims, suggest search terms, compare two passages, or turn a long document into a checklist. It cannot decide that a source is authoritative for your purpose without human judgment. The House of Commons Library recommends precise identification of each fact and verification with an independent source, with human oversight rather than treating generated text as a replacement for trusted sources or professional judgment.
For high-stakes decisions, ask a qualified professional to review the evidence. A medical, legal, financial, safety, or public-policy answer may require expertise that a general reader cannot substitute with more searching.
Provenance tools can add useful signals. NIST’s synthetic-content work describes provenance, authentication, labeling, detection, testing, auditing, and maintenance as complementary approaches. Provenance metadata can indicate where a file came from or how it was transformed, but it does not prove that the underlying claim is true. You still have to read the source and evaluate its evidence.
CDC guidance states: “AI tools cannot be authors or coauthors and should not be cited as authors or primary sources.” Cite the human-authored, institutional, legal, statistical, or scientific source that actually supports the statement. If an AI system helped locate or summarise material, disclose that role where the relevant publication or workplace rules require it.
A reusable audit worksheet
Copy this structure into a document or spreadsheet for each response:
| Field | What to enter |
|---|---|
| Claim ID and exact wording | One atomic assertion copied from the AI answer |
| Type | Fact, interpretation, calculation, prediction, or opinion |
| Priority | High, medium, or low, based on consequence, centrality, novelty, and time sensitivity |
| Cited source | Exact title, author or organisation, date, edition, URL, and document identifier |
| Evidence location | Page, section, table, paragraph, dataset row, or quoted passage |
| Scope | Jurisdiction, population, timeframe, version, and definitions |
| Independent check | Separate authoritative source and what it confirms or disputes |
| Status | Verified, partly supported, unsupported, contradicted, or unresolved |
| Notes | Limitations, conflicts, access date, and the wording you would publish |
Common failure modes and the fix for each
Confident prose mistaken for evidence
Failure: The response is detailed and decisive, so the reader assumes it was researched. Fix: Ignore tone during the first pass and extract claims mechanically.
A real source attached to the wrong claim
Failure: The link opens, but supports only a narrower or different statement. Fix: Apply the entailment and context tests to the entire sentence.
Invented or mismatched citations
Failure: The title, author, DOI, or quotation cannot be found. Fix: Search the issuing organisation’s archive and mark the claim unsupported if the document is absent.
Search snippets treated as proof
Failure: A snippet appears to confirm the answer but omits a limitation or refers to an old page. Fix: open the full document and cite the passage in context.
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Several copies mistaken for independent confirmation
Failure: Multiple websites repeat one press release or one unsourced paragraph. Fix: trace the chain to the original record and seek a separate source.
AI detection confused with fact-checking
Failure: A detector labels text as human- or AI-written, and the reader treats that as a truth judgment. Fix: evaluate claims and evidence directly; authorship detection does not establish accuracy.
Uncertainty hidden by a rewrite
Failure: A reviewer silently changes “may” to “does” or fills a missing date from memory. Fix: preserve the source’s uncertainty and state what remains unverified.
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You do not need to verify every harmless sentence to the same depth. Escalate when a wrong answer could cause physical harm, legal exposure, financial loss, privacy damage, or a consequential public decision. Also escalate when sources conflict, the governing document is unclear, the evidence is inaccessible, or the answer depends on specialist methodology.
For a low-stakes fact, a primary source and a quick context check may be sufficient. For a high-stakes claim, require direct evidence, independent corroboration, explicit scope and date, and qualified review. Keep the unresolved status if those conditions cannot be met.
A concise decision rule
- Can you identify the exact claim?
- Does the cited source exist and match the identity details?
- Does the source support every part of the wording in context?
- Is it authoritative and appropriate for this claim?
- Does an independent source agree within the same scope?
- Have you recorded the date, version, jurisdiction, population, and limitations?
If any answer is no, narrow the wording, add the missing qualification, seek better evidence, or label the claim unresolved. That is a more reliable outcome than accepting a citation because it looks convincing.
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