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Do not treat an AI answer as an authority. Treat it as a bundle of claims. Before you repeat, publish, or act on those claims, identify what matters, inspect suitable original or authoritative evidence, verify that citations exist and support the exact statements attached to them, check dates and context, and obtain independent confirmation or expert review when the consequences justify it.
Why a fluent AI answer can still be wrong
Generative AI produces likely-sounding text, not guaranteed truth. It can invent facts, definitions, dates, quotations, references and calculations; omit important cases; apply a rule to the wrong jurisdiction or software version; or make an ambiguous question sound settled. The result may be coherent and confident while being false. The UK Government’s Artificial Intelligence Playbook describes this failure as a hallucination: a response that appears truthful but is not.
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OpenAI’s Help Center guidance similarly warns that confidence is not reliability. It advises checking important information, quotations, data, technical details and references by visiting sources directly. The practical implication is simple: the smoother an answer sounds, the less useful that smoothness is as evidence.
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1Clear out junk files and repair common Windows errors2Fix the driver behind crashes, sound loss and screen glitches3Repair Windows errors before they cause bigger problemsVerification is not a ritual that makes every answer certain. It is a decision process for finding which claims are supported, which need qualification, and which should be removed.
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Start with the consequences of being wrong
Choose the depth of checking before you inspect the wording. A typo in a brainstorming list and an incorrect medical instruction should not receive the same review.
| Use case | Minimum sensible check | Escalate when |
|---|---|---|
| Low-stakes ideas, outlines or creative drafts | Check any factual details you plan to retain; label uncertain material as a lead rather than a fact. | A detail will be published, attributed to a person, or used to make a decision. |
| Public article, presentation or academic work | Verify every material fact, number, quotation and citation against the underlying source. | The claim is contested, recent, quantitative or central to your conclusion. |
| Money, employment, education or business decisions | Use current primary records, official guidance or contractual documents and check assumptions. | The decision affects a person’s rights, eligibility, finances or reputation. |
| Health, law, safety or government services | Do not rely on the generated answer alone. Consult the applicable authority and a qualified professional. | Any action could cause injury, legal exposure, loss of rights or irreversible harm. |
There is no universal number of citations that guarantees correctness. The appropriate effort depends on the claim, the source quality and the cost of an error.
Step 1: Define what you will use the answer for
Write down the intended action: publish a paragraph, submit an assignment, configure software, choose a treatment, interpret a law or estimate a cost. This exposes hidden requirements such as a date range, country, population, product edition or risk tolerance.
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- Note whether the answer will describe a current state or a historical one.
- List any version, platform, dataset, contract or policy that limits applicability.
- Decide which mistakes would be merely inconvenient and which would change the decision.
If you cannot state the intended use, you cannot judge whether a source is fit for purpose.
Step 2: Turn the response into individual claims
Do not fact-check a whole paragraph as if it were one proposition. Mark each independently checkable item, including statements that look minor.
- Names, dates, locations, definitions and causal explanations.
- Numbers, percentages, rankings, totals, units and calculations.
- Quotations, attributed opinions and claims about what a person or organization said.
- References, links, document titles, publication dates and version numbers.
- Descriptions of code behavior, commands, settings and compatibility.
- Assumptions about the reader’s situation, eligibility, population or jurisdiction.
- Words such as “always,” “never,” “guaranteed,” “safe,” “best” or “the only,” which often hide exceptions.
Rewrite a broad sentence into testable questions. “This update improves security” becomes: Which release? Which vulnerability? What change was made? Who documented it? Under what conditions?
Separate factual assertions from interpretation and advice. An answer may correctly report a statistic but then draw an unsupported conclusion from it. Check the reported fact, the inference, and the recommendation as three different claims.
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Step 3: Select evidence that fits the claim
Relevance and reliability are different. A page can discuss the right topic and still be outdated, incomplete or misapplied. Choose the evidence type that is closest to the fact being asserted.
| Claim | Preferred evidence | What to verify |
|---|---|---|
| Government figure or service rule | Official statistics, the responsible department or the current service guidance | Release date, definition, geography, denominator and revisions |
| What a law requires | Primary legislation, regulation, court decision or official legal guidance | Jurisdiction, effective date, exceptions and whether later amendments apply |
| Research finding | Original peer-reviewed paper, dataset or author correction | Study design, population, sample size, measured outcome and limitations |
| Software behavior | Applicable technical documentation, release notes, source code or a reproducible test | Version, operating system, configuration, dependencies and expected output |
| Historical event | Contemporaneous primary records and reputable archival or scholarly synthesis | Date, provenance, competing interpretations and quotation accuracy |
The House of Commons Library recommends matching the source to the fact: an official record for a government number, primary legal text for what a law says, the original paper for a research result, and applicable technical documentation or testing for software behavior.
Compare candidate sources on five dimensions: authority for this particular claim, originality, recency, independence from the AI answer, and fit to the relevant jurisdiction, population, version and method. A prominent secondary article may be less useful than a modest official table if the question concerns an exact government figure. Conversely, an official page may not settle a contested scientific interpretation that requires the original study and independent scholarship.
If the answer supplies no usable citation, treat it as a lead. Search for the underlying record yourself using distinctive names, dates, document titles or quoted phrases, then apply the same source and claim checks. A search-result snippet, autogenerated summary or plausible title is not evidence until you open and inspect the underlying document.
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A citation is an invitation to inspect evidence, not proof that the claim is true. Open the linked document rather than relying on a search-result snippet, a title, or the AI’s description of it.
- Confirm that the source exists. Check the publisher, document title, author, publication date and stable location. A plausible-looking reference may be fabricated.
- Find the relevant passage or table. Search the document for the key term, number or quotation. Read enough surrounding context to capture definitions and qualifications.
- Compare the exact proposition. Ask whether the source supports the same wording, scope and certainty. A source that says “may be associated with” does not support “causes.” A page about one product version does not prove behavior in another.
- Check status. Look for corrections, retractions, superseded editions, archived notices and amendments.
- Record the result. Mark the claim supported, partially supported, contradicted, unverifiable or not applicable, and keep a note of the passage you relied on.
Do not accept a reference merely because its title sounds right. AI can attach a real source to a different claim, misstate its conclusion or combine details from several documents.
Check the source’s metadata as carefully as its prose. Confirm whether a page is a draft, summary, press release, archived copy or current version. For a dataset, inspect its methodology and revision notes; for a paper, look for an author correction or retraction notice; for a legal text, check amendments and commencement dates.
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Step 5: Test time, context and applicability
Many apparent errors are context errors. A true statement can be wrong for the reader’s date, location, population or version.
- Recency: Check publication and update dates for prices, policies, security advice, statistics, product features and eligibility rules.
- Definitions: Confirm what terms such as “income,” “user,” “accuracy,” “supported” or “available” mean in the source.
- Scope: Match the population, sample, language, industry and operating conditions.
- Jurisdiction: Laws, taxes, public services and professional rules differ by country and sometimes by state or municipality.
- Version: Tie technical instructions to the exact release, platform and configuration.
- Uncertainty: Preserve margins of error, confidence intervals, limitations and competing interpretations rather than converting them into certainty.
- Missing cases: Look for exclusions, edge cases, counterexamples and conditions under which the claim fails.
For current or contested claims, seek confirmation from a separate suitable source. Two AI responses are not independent evidence: they can reproduce the same training-data error or prompt assumption.
Independence matters even when two sources disagree. Two websites that copy the same press release are not independent confirmations. Trace important claims back to separate records, methods or institutions, and explain a disagreement rather than averaging it away.
Step 6: Check calculations, quotations and technical instructions
Numbers and calculations
Recalculate figures yourself or with a transparent, appropriate method. Check units, rounding, currency, denominator, time period and whether a percentage is relative or percentage-point change. Ask where the input values came from and whether the calculation silently assumes missing data.
Write the calculation in a form another person can reproduce. A claim that a change is “20%” may mean a relative reduction, a 20-percentage-point difference, or a rounded estimate. Verify the baseline, comparison group and date before repeating it.
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Quotations and attribution
Locate the original speech, interview, paper or transcript. Compare the wording, speaker, date and context. If you cannot find the original, do not present the sentence as a verbatim quotation. Paraphrase only what a reliable source actually establishes, or remove the attribution.
Code and commands
Read the documentation for the stated version and run a safe, isolated test when possible. Check prerequisites, permissions, dependencies, input format and expected output. Never execute an unfamiliar command on production data merely because an AI labels it safe; make a backup and understand each operation first.
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What does not prove an answer is correct
- Fluency: Smooth grammar shows language generation, not verification.
- Confidence: Emphatic wording is not a calibrated probability of truth.
- Consistency: Repeating the same answer can repeat the same systematic error.
- A citation: The reference may be nonexistent, irrelevant, outdated or unable to support the attached statement.
- Grounding or retrieval: Supplied material can still be retrieved or interpreted incorrectly.
- AI-text detector scores: Detection tools produce indicators about writing patterns, not proof that individual facts are true or false.
- Provenance labels: Knowing that content was generated, edited or copied helps establish origin, not the truth of each claim.
NIST’s report Reducing Risks Posed by Synthetic Content: An Overview of Technical Approaches to Digital Content Transparency (NIST AI 100-4, published 20 November 2024 and with its publication page updated 8 April 2026) surveys provenance, labeling, detection and auditing approaches. Those approaches can help answer where content came from or whether it may be synthetic; they do not replace claim-level evidence checks.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How to resolve claims that do not check out
Use the evidence, not the original confidence of the AI, to decide what remains.
- Keep a claim when an appropriate source directly supports its wording and scope.
- Qualify a partial claim by adding the applicable date, population, jurisdiction, version, uncertainty or exception.
- Correct a claim when reliable evidence contradicts it, and recheck nearby claims that depended on the same assumption.
- Remove a claim when you cannot establish it or when the only support is a fabricated, unsuitable or inaccessible reference.
- Escalate consequential material to a subject expert who can assess the evidence and take responsibility for the final decision.
Keep a simple audit trail for important work: the original AI output, each claim, the source checked, the relevant passage, the date you checked it and the final wording. This makes later updates and corrections possible.
When you revise a claim, preserve the reason for the change. “Updated because the agency revised its dataset” is more useful than silently replacing a number. If a source cannot be accessed or its status is unclear, mark the claim unresolved rather than implying that it passed.
A practical example
Example: a software compatibility claim
An AI answer says, “Feature X works on all versions after 3.0,” and cites a documentation page. Split the statement into version, feature and platform claims. Open the documentation, identify the page’s update date and supported versions, and check release notes for changes. If the page covers only the web edition, do not apply it to the desktop edition. Run a test in a non-production environment with the reader’s version. The final text might state only the versions and platform the documentation and test support, with an explicit limitation for untested configurations.
Example: a public statistic
An answer reports that a program “cut unemployment by 20%.” Find the official dataset or evaluation, determine whether 20% means a relative reduction or 20 percentage points, inspect the population and comparison period, and read the methodology. If the evaluation found an association rather than a causal effect, preserve that distinction. If the statistic has since been revised, use the current release and date it.
Example: health, legal or financial guidance
Do not convert a general AI explanation into a personal instruction. Identify the applicable official guidance, verify the current rule for the reader’s jurisdiction and circumstances, and ask a qualified clinician, lawyer or financial professional to review a consequential decision. If those checks are unavailable, do not act on the generated answer.
A reusable claim-checking checklist
- What decision or publication will this answer influence?
- Which exact sentences contain factual claims?
- Have I separated facts, assumptions, opinions and recommendations?
- Does each important claim have an appropriate, identifiable source?
- Did I open the source and read the relevant passage?
- Does it support the exact wording, not merely the topic?
- Are the date, jurisdiction, population, edition and method applicable?
- Did I check definitions, exceptions, uncertainty and counterevidence?
- Did I independently confirm a current or contested claim?
- Did I recalculate numbers and verify quotations or code safely?
- Have I corrected, qualified or removed unsupported material?
- Does a qualified expert need to review the final version?
How much checking is enough?
Stop when every material claim has evidence appropriate to its risk, the limits are visible to the reader, and unresolved uncertainty is not hidden. Low-stakes drafts may need only targeted checks. Public, high-impact or irreversible decisions require deeper source review, independent confirmation and, where appropriate, expert sign-off. The goal is not to prove that an AI system is generally right or wrong; it is to ensure that the particular claims you are about to use can withstand inspection.
Frequently Asked Questions
Should I ask another AI model whether the first answer is correct?
Use another model only to suggest questions or possible sources. Repeated AI answers are not independent evidence because systems can share training-data errors or the same prompt assumption. Check the underlying official, primary or technical source instead.
What if an AI citation looks real but I cannot find the quoted passage?
Treat the claim as unverified. Check the publisher, title, date and stable link, search the full document, and look for corrections or a superseded version. Do not present the quotation or claim as established unless the source supports it.
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There is no universal number. Match the effort to the claim and the cost of being wrong. A routine fact may need one suitable primary source; a contested or high-consequence claim may require independent confirmation and qualified expert review.
Can an AI detector tell me whether the answer is factual?
No. Detector scores concern writing patterns or possible synthetic origin, not whether individual claims are true. Provenance and detection tools can provide context about where content came from, but claim-level evidence checking is still required.
What should I do with a partly correct answer?
Keep statements directly supported by suitable evidence, qualify those that need a date, scope or uncertainty, correct contradictions, and remove claims you cannot establish. Recheck nearby claims that depended on the same faulty assumption.
Is it safe to follow AI advice about health, law or money?
Do not rely on a generated answer alone for consequential action. Verify the current rule or guidance for your jurisdiction and circumstances, then consult an appropriately qualified clinician, lawyer or financial professional.
The Bottom Line
Check AI output claim by claim: open the underlying sources, verify exact support, date, context and applicability, and recalculate or test technical details. Keep, qualify, correct or remove each claim based on evidence—and use qualified human review when the consequences are serious.
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