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Do not treat an AI answer as an authority. Treat it as a bundle of individual claims: identify the claims that matter, open and evaluate the underlying evidence, check dates and context, seek independent confirmation when the consequences justify it, and then keep, correct, qualify, or remove each claim. The more an error could cost you—in publication, money, health, legal rights, safety, or another person’s prospects—the more rigorous the review and the more appropriate qualified human oversight become.
Why a fluent AI answer can still be wrong
Generative AI produces likely sequences of words, not a guaranteed record of reality. It can state a false fact, use the wrong definition, transpose a date or number, invent a quotation or reference, present only one side of an ambiguous question, or attach a genuine source to a claim that the source does not support. These failures often read smoothly and confidently, which makes them easy to miss.
OpenAI’s Help Center guidance on whether ChatGPT tells the truth cautions that confidence is not reliability. It recommends checking important information, quotations, data, technical details, and references by consulting reliable sources directly. The UK Government’s Artificial Intelligence Playbook describes a hallucination as an output that appears truthful but is false and warns that uncritical or exclusive reliance can mislead people about public services, policy, or law.
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Fluency, emphatic wording, a repeated answer, a citation, retrieval of relevant material, or a high score from an AI-text detector is not proof that a claim is true. Each is at most a signal or a lead. Accuracy is established by evidence that fits the particular claim.
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Start by deciding what is at stake
The right amount of checking depends on the harm an error could cause and on how reversible your decision is. Before reading line by line, write down how you intend to use the output.
| Intended use | Typical review | Extra safeguard |
|---|---|---|
| Brainstorming, headings, or other disposable ideas | Check that the text does not smuggle in factual claims you might later repeat. | Label unverified ideas and verify anything promoted into a final draft. |
| Routine factual explanation or internal note | Check each material fact against an appropriate source and confirm dates, units, and definitions. | Have another person spot-check claims that guide a decision. |
| Published, academic, or client-facing work | Verify every important assertion, quotation, statistic, citation, and interpretation at claim level. | Keep an evidence record and obtain subject review for specialist content. |
| Medical, legal, financial, safety, employment, or public-sector decisions | Use authoritative records and more than one suitable source when uncertainty or disagreement matters. | Ask a qualified professional to review the proposed action; do not rely on generated text alone. |
This is a risk-based process, not a hunt for a universal citation count. No fixed number of sources guarantees correctness. Authority, originality, recency, independence, and fit to the question matter more than a total.
Step 1: Turn the answer into a claim inventory
Copy the AI output into a working document and mark statements that could be checked. Separate a paragraph’s prose into small units rather than treating its polished presentation as evidence.
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- Dates, figures, and units: publication dates, prices, percentages, limits, measurements, currency, and time zones.
- Quotations and attributions: words supposedly spoken or written by a person or organization.
- References and links: article titles, authors, document numbers, URLs, and claims about what a source says.
- Calculations: arithmetic, conversions, growth rates, totals, and assumptions embedded in a result.
- Code and technical behavior: what an API, command, configuration, or version is said to do.
- Interpretations and recommendations: conclusions that depend on evidence, a jurisdiction, a population, a software version, or an unstated assumption.
Mark the claims that would change your decision if they were false. Those receive the deepest checks first. A harmless example or rhetorical transition does not deserve the same effort as a legal deadline, a dosage, or a performance figure.
Step 2: Match each claim to the right evidence
Choose a source because it is suitable for the claim, not merely because it mentions the same subject. The House of Commons Library lists official statistics, primary legislation, government departments, recognized regulators, peer-reviewed research, and its own research briefings as examples of reputable sources. The best choice depends on the question.
| Claim you need to verify | Prefer this evidence | What to check |
|---|---|---|
| A government figure or service rule | The responsible department’s current page, official dataset, or published record | Release date, revision status, geography, definition, and units |
| What a law or regulation requires | The primary legal text or an official regulator’s current guidance | Jurisdiction, effective date, exceptions, and whether the text is in force |
| A research finding | The original paper, supplementary material, or a recognized scholarly database | Study design, population, outcome, limitations, and whether a later correction or retraction exists |
| Software behavior | Version-specific technical documentation, release notes, source code, or a reproducible test | Product edition, version, platform, prerequisites, defaults, and observed result |
| A historical event or quotation | An archival record, transcript, official publication, or reputable primary collection | Exact wording, speaker, date, translation, and surrounding context |
A secondary summary can help you find the primary record, but it is not a substitute when the original is available. A search-result snippet or plausible document title is only a lead until you open the source.
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Step 3: Open every important citation and test the connection
Do not assume a reference exists because its title sounds credible. Follow the link or search for the publication through the issuing organization’s site. Record whether the document is real, who issued it, when it was published or updated, and whether it has been corrected, withdrawn, or superseded.
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- Locate the relevant passage, table, or method. Read enough surrounding text to see definitions, qualifications, sample selection, and exceptions. A source can be genuine while the AI’s summary is wrong.
- Compare the exact proposition. Ask whether the source supports the same subject, action, quantity, time period, and degree of certainty. “Discusses” is not the same as “demonstrates,” and a correlation is not automatically a cause.
- Check status and corrections. Look for errata, retractions, updated guidance, version history, or a notice that the material applies only to a particular edition or jurisdiction.
- Write a traceable note. Store the source, page or section, access date, and the precise claim it supports. If the connection is partial, record the limitation instead of silently broadening it.
When no citation is supplied, the same procedure still applies: formulate a search for the claim, find an appropriate original or authoritative record, and inspect it directly. Absence of a citation is a reason to investigate, not proof that the statement is false.
Step 4: Check time, scope, and hidden assumptions
A statement can have been accurate once and still be unusable now. For each material claim, ask:
- When? Could a policy, price, specification, scientific conclusion, or service have changed? Use the newest applicable record and note the “as of” date.
- Where? Does the evidence apply to your country, state, regulator, market, or legal jurisdiction?
- Who? Is the population, customer group, age range, or organization the same as in the source?
- Which version? Do the product edition, operating system, API release, model, or standard match the situation in the answer?
- How was it measured? Check the method, sample, baseline, units, and uncertainty. A laboratory result or a one-time allowance should not be rewritten as a universal promise.
- What is missing? Look for exclusions, counterexamples, competing interpretations, and conditions under which the claim fails.
For current or contested matters, obtain independent confirmation from a separate suitable source. Independence matters: two pages that copy the same press release, or two AI answers prompted with the same information, do not provide two independent confirmations.
Step 5: Verify calculations, quotations, and technical instructions separately
Recalculate numbers
Recompute arithmetic with a calculator, spreadsheet, or a transparent script. Check whether the AI used the right base, period, rounding rule, currency, unit conversion, or denominator. Reconcile totals with the source table instead of accepting a plausible-looking percentage. Preserve the formula and inputs so another reviewer can reproduce the result.
Authenticate quotations
Search for the exact wording in an authoritative transcript, publication, recording, or archival collection. Confirm that the speaker, date, language, and surrounding passage match. If only a paraphrase can be supported, remove quotation marks and identify it as a paraphrase. Never fill a missing quotation with wording that merely sounds like the person.
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Test code and technical behavior
Read the documentation for the exact version and platform, then run a safe, minimal test in an isolated environment. Check inputs, permissions, defaults, error handling, and the output—not just whether the command executes. A code sample that works in one release can fail or have a different security implication in another. Do not run untrusted generated commands against production systems or sensitive data before a qualified review.
Inspect recommendations and arguments
Separate evidence from interpretation. Identify the assumptions that lead from facts to a recommendation and ask whether another reasonable assumption changes the result. An answer may cite real material while still drawing a conclusion that the material does not warrant.
What citations, grounding, and detectors can—and cannot—tell you
A citation gives you something to inspect; it does not establish that the source exists or supports the attached sentence. Retrieval or “grounding” can supply relevant documents while the model still selects the wrong passage, misunderstands a qualification, or applies information outside its scope. Read the evidence at claim level.
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AI-text detectors look for linguistic patterns associated with generated writing. The House of Commons Library notes that such tools are not definitive and can misclassify human or AI-assisted text. 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; publication page updated 8 April 2026) surveys provenance, labeling, detection, and auditing approaches. Provenance can help establish where content came from or whether it was marked as synthetic; it does not prove that every factual claim in the content is true. Use evidence checks for accuracy.
Resolve each claim explicitly
After checking, assign one of four outcomes and edit the answer accordingly:
- Keep: The source is genuine, directly supports the statement, and fits the relevant date, scope, and method.
- Qualify: The evidence supports a narrower statement. Add the applicable population, jurisdiction, version, uncertainty, date, or limitation.
- Correct: Replace a wrong number, definition, date, calculation, or description with the verified one, retaining a note about the correction when your workflow requires it.
- Remove: Delete claims that cannot be substantiated, references that do not exist, quotations that cannot be authenticated, and recommendations whose assumptions do not survive review.
Do not leave an unsupported sentence in place merely because most of the surrounding paragraph is accurate. A fluent answer can hide one consequential error among several true statements.
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Use a repeatable record for important work
For publication, research, client work, or a decision that may be audited, keep a simple claim log. It can be a spreadsheet or a document with these columns:
| Field | Entry to record |
|---|---|
| Claim ID | A short label that lets reviewers find the sentence in the draft. |
| Original wording | The exact AI-generated claim before editing. |
| Risk and intended use | What could happen if it is wrong and who will rely on it. |
| Evidence | Publisher, title, URL or document identifier, relevant section, and access date. |
| Fit check | Date, jurisdiction, population, version, method, and known limitations. |
| Decision | Keep, qualify, correct, or remove, with the final wording. |
| Reviewer | The person who checked it and any unresolved question requiring expertise. |
This record prevents citation drift, makes later updates possible, and shows which statements still need a human decision. It is especially useful when a source changes after the draft is written.
When to involve a subject expert
Ask a qualified human reviewer when the answer concerns diagnosis or treatment, legal rights or deadlines, investments or taxes, physical safety, regulated compliance, employment or admissions decisions, or another person’s access to services. Give the reviewer the original output, your claim log, the sources you found, and the exact action you are considering. The expert should assess both the evidence and whether it applies to the individual circumstances; generated prose is not a substitute for that judgment.
For high-consequence work, the final decision-maker remains responsible for the result. If authoritative sources conflict, preserve the disagreement, explain which rule or evidence you used, and seek specialist advice rather than averaging the claims or asking another AI system to choose.
Common checking mistakes and their fixes
“It sounds right, so I kept it.”
Fix: Highlight every consequential assertion and require a source or a clearly labeled uncertainty statement before publication or action.
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“The link works, so the citation is valid.”
Fix: Read the cited passage and compare the exact subject, number, date, and certainty of the source with the sentence that cites it.
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“Several sites repeat the same fact.”
Fix: Trace the repetition back to the original record and determine whether the sites are independent or copying one another.
“Two AI tools gave the same answer.”
Fix: Treat agreement as a prompt to inspect evidence, not as corroboration. Shared training material or a shared error can produce identical responses.
“A detector says the passage is human or AI-generated.”
Fix: Ignore the label when deciding whether the facts are correct. Check provenance for origin questions and sources for truth questions.
“I checked the source but not its conditions.”
Fix: Record the source’s date, jurisdiction, population, version, method, exceptions, and uncertainty alongside the claim.
A practical final checklist
- Have I stated how the output will be used and what an error could cost?
- Did I split the answer into individual facts, numbers, quotations, citations, calculations, technical behaviors, and assumptions?
- Does each important claim have evidence appropriate to its subject?
- Did I open the source, verify that it exists, and read the relevant passage?
- Does the source support the exact claim rather than merely the general topic?
- Are date, jurisdiction, population, version, method, units, exceptions, and uncertainty applicable?
- Did I recalculate numbers, authenticate quotations, and test code safely?
- For a current or disputed issue, did I seek genuinely independent confirmation?
- Did I keep, qualify, correct, or remove every claim explicitly?
- Does a qualified person need to review the final decision or advice?
The safest habit is simple: use AI to accelerate drafting and exploration, then make the evidence—not the model’s confidence—the basis for what you repeat or do.
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