How do I fact-check AI-generated content before using it at work? Treat every generated fact, figure, quotation, date and citation as a lead—not as established truth. Break the output into individual claims, verify each against an appropriate independent source, test its date and context, have an expert review high-consequence material, and keep a claim-to-evidence record. An AI detector can suggest that text merits scrutiny, but it cannot prove that a claim is true.
Why generated workplace content needs claim-level checking
Generative AI is useful for brainstorming, summarising material and improving wording. It is less dependable when asked for definitive factual answers, contested or politically sensitive analysis, interpretations of law, policy or procedure, or supposedly impartial conclusions. A fluent paragraph can therefore contain a mixture of accurate information, outdated details, unsupported inferences and invented citations.
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The practical rule is simple: no factual statement becomes publishable merely because an AI system wrote it confidently or attached a link. The person or team that sends, publishes or acts on the material remains responsible for it.
Chris Rhodes, author of the House of Commons Library briefing Working with AI and spotting AI-generated text (19 May 2026), puts the safeguard plainly: “The best guard against hallucinations from AI is to check everything generated carefully, ideally with an expert.”
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First separate authorship detection from truth verification
Two different questions are often confused:
- Could AI have produced this text? This is an authorship or provenance question.
- Are the statements in it accurate, current and properly supported? This is a fact-checking question.
AI-detector scores, unusual phrasing, vague prose, weak citations and other stylistic clues may justify a closer review. They do not establish that a passage was written by AI, and they do not prove that a claim is false. The House of Commons Library describes AI detectors as unreliable and not conclusive. NIST evaluations that test content-authorship detection are measuring a different task from factual correctness.
For images, audio and video, metadata, watermarks and provenance systems can provide clues about origin or editing history. NIST AI 100-4 (20 November 2024) cautions that transparency about origin does not guarantee trustworthiness: authentic material can be removed from its original context or manipulated after creation. Check the underlying event, speaker, document and date even when a provenance signal is present.
The eight-step workplace verification workflow
1. Decide whether AI is suitable for the task
Before checking a draft, define what you intend to do with it and what an error would cost. AI assistance may be reasonable for a low-risk brainstorm or a first-pass summary that will be rewritten. It is not a substitute for professional judgment in a medical, financial, legal, employment, safety, regulatory or other high-consequence decision.
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- Classify the likely harm from an error as low, moderate or high.
- Increase the review level when the subject is specialised, contested, sensitive, time-critical or regulated.
- If the task requires a licensed or accountable professional judgment, use AI only for assistive work and retain qualified human sign-off.
2. Break the output into checkable claims
Do not fact-check an entire page as one block. Copy the output into a review document and mark each discrete assertion. Include:
- names of people, organisations, products and places;
- dates, timelines, prices, percentages, measurements and other numbers;
- quotations and claims about what someone said or did;
- causal explanations (“X caused Y”) and predictions presented as likely outcomes;
- statements about law, policy, procedure, current events or regulatory status;
- claims that a study, dataset or authority reached a particular conclusion.
Label opinion, recommendation and interpretation separately. They may need transparent reasoning and disclosure of assumptions, but they are not verified in the same way as a date or statistic. Split compound sentences: a sentence can contain a true event, an unsupported cause and an incorrect number.
3. Inspect every supplied citation yourself
Open every link rather than trusting the title, URL or citation text generated beside it. For each source, record:
- Whether the page or document genuinely exists and comes from the named author or issuing body.
- The publication or update date and the jurisdiction or edition it covers.
- The exact passage, table or data point that is supposed to support the claim.
- Whether the passage supports the wording, scope and certainty of the AI’s statement.
A genuine source can still be irrelevant, obsolete or misrepresented. A report about one country does not automatically support a claim about another; a headline does not establish every conclusion in an article; and a source that discusses correlation does not by itself prove causation. Save the relevant passage or page reference in your audit record so another reviewer can reproduce the check.
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Do not limit the check to links the model supplied. Search for the original evidence using the claim’s key terms, date, location and issuing body. Choose the source that is closest to the fact being asserted:
| Claim type | Strong starting evidence | What to confirm |
|---|---|---|
| Law, regulation or official procedure | Current legislation, the relevant government department or regulator | Jurisdiction, effective date, exceptions and whether guidance is mandatory |
| Official figure or policy result | Official statistics, administrative records or the agency’s original release | Definition, population, period, units, revisions and methodology |
| Research finding | Original peer-reviewed paper, study, dataset or preprint where appropriate | Sample, design, limitations, measured outcome and whether the claim overstates causality |
| Quoted statement | Original speech, transcript, filing, interview or document | Exact wording, speaker, date and surrounding context |
| Historical or current event | Contemporaneous primary records plus independent authoritative reporting | Event date, location, sequence and whether later corrections changed the account |
The House of Commons Library lists official statistics, primary legislation, government departments, recognised regulators, peer-reviewed research and its own briefings as examples of reputable evidence. Treat that list as a set of options, not an automatic ranking: the right source depends on the claim and jurisdiction. Use more than one independent source when the issue is disputed or consequential, and check whether apparently separate pages all copy the same original report.
5. Test time, scope and context
Many errors are not wholly invented; they are true statements applied to the wrong time, place or population. For every number or rule, write down:
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- the relevant date or period and the source’s last-update date;
- the geography, organisation, edition or legal jurisdiction;
- the population or sample and the definition used;
- the units, denominator, baseline and rounding method;
- whether the figure is a one-time result, a forecast, a recurring allowance or a promotional value.
Check the surrounding paragraphs, footnotes, appendices and methodology. A quotation should be compared with the original speaker or document, preserving words that qualify or limit the statement. If the source has been superseded, use the current version and explain the change rather than silently carrying over the old wording.
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6. Look for omissions and overstatement
Verification asks more than “Is there a source?” NIST’s evaluation-probe work describes three useful tests:
- Faithfulness: does the source actually support the claim?
- Completeness: does the output preserve the source’s full message, including material caveats?
- Sufficiency: is the source strong enough to carry the confidence and importance of the wording?
Apply those tests sentence by sentence. Replace “proves” with “suggests” when the evidence is correlational, add a limitation that the draft omitted, narrow a broad generalisation to the studied population, or remove the claim when the source cannot support it. Look actively for contrary evidence and reasonable alternative explanations, not only confirmation.
7. Escalate review according to risk
Ask a subject-matter expert to review specialised, contested, sensitive or consequential material. NIST AI RMF 1.0 emphasises evaluating findings in relation to intended use and deployment conditions; an expert can judge whether a technically accurate sentence is still misleading in practice.
| Risk level | Minimum control | Examples of escalation |
|---|---|---|
| Low | Claim list, source check and editor review | Internal brainstorming, non-critical background wording |
| Moderate | Independent corroboration and documented evidence | Customer education, operational guidance, public-facing statistics |
| High | Qualified subject expert, current primary sources and accountable sign-off | Legal or regulatory advice, safety instructions, financial or employment decisions, contested public claims |
If evidence is unavailable or credible sources conflict, qualify the statement, present the disagreement with its dates and jurisdictions, or omit it. Do not fill a gap with plausible-sounding language.
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The reviewer—not the model—decides what remains. Correct errors, rewrite for the intended audience, distinguish evidence from interpretation and disclose meaningful uncertainty. Keep a compact record for important claims:
| Field | Entry to preserve |
|---|---|
| Claim ID | Short label and the exact sentence used in the final draft |
| Evidence | Source title, issuing body, author, date, page or section and link if applicable |
| Support | Quoted passage, table row or data definition that supports the wording |
| Checks | Date, jurisdiction, units, population, context and independent corroboration |
| Decision | Keep, narrow, qualify, correct or remove |
| Reviewer | Name or team, review date and any unresolved limitation |
This record makes later updates possible and shows why a claim was accepted. It is especially valuable when a source changes, a regulation is amended or a reader challenges a published statement.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How to check common high-risk claim types
Statistics and percentages
Locate the original table or dataset. Verify the numerator, denominator, sample, time period, geography and whether the percentage is weighted, estimated or revised. Recalculate simple percentages when feasible, and do not present a relative change as an absolute number. If the source reports a range or margin of error, retain it.
Law, policy and procedure
Use the current text from the relevant legislature, department or regulator. Confirm the jurisdiction, effective date, definitions, exemptions and enforcement status. A general explainer may provide context, but it should not replace the governing instrument for a decision that carries legal or operational consequences.
Research and medical or technical claims
Read the study’s methods and limitations, not just an abstract or press release. Check whether the result is observational, experimental, replicated, peer reviewed and applicable to the population in your draft. Preserve uncertainty where the authors do, and ask a qualified specialist to interpret material findings.
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Quotes and attributions
Find the original transcript, recording, filing or document. Confirm the speaker, date and exact wording, then read enough surrounding context to detect irony, conditions or a statement about someone else’s view. If the original cannot be located, do not use quotation marks; attribute only what can be established.
Images, audio and video
Check when and where the media was created, who first published it and whether an independent record confirms the event. Reverse-image-searching a picture, as recommended in Indiana DHS’s general misinformation guidance, can reveal an older or different context. Metadata or watermarking can support a provenance assessment but cannot establish that the depicted event or accompanying claim is truthful.
What to do when sources disagree
First make sure the disagreement is real rather than a difference in date, definition, units or jurisdiction. Then prefer the source with the closest access to the original evidence and the clearest method, while documenting why. If reputable sources still conflict, state the conflict and the reasons for it, use wording that reflects the uncertainty, and identify what future evidence could resolve it. A transparent “the available sources disagree” is more accurate than a single confident answer chosen because it sounds neat.
Common failure modes and recovery steps
- Trusting a polished paragraph: extract and check individual claims; style is not evidence.
- Counting search results: trace results back to original records and assess independence rather than popularity.
- Checking only the link: read the cited passage and compare its scope with the sentence.
- Using an old but genuine source: check update and effective dates, then replace or qualify it.
- Letting a detector decide: use detector output only as a prompt for review; verify the claims directly.
- Removing caveats for readability: restore limitations that change the practical meaning.
- Leaving unsupported gaps: narrow, omit or label uncertainty instead of guessing.
- Skipping ownership: assign a named reviewer and retain the evidence record before publication or action.
Building a repeatable team process
Create a shared claim-review template with the fields above and require it for high-risk outputs. Set review triggers for legal or policy language, current events, numerical claims, quotations, external communications and decisions affecting people. Store the source passage, review date and unresolved limitations alongside the final document, not only in a chat history that may be difficult to retrieve.
Train staff to ask six questions of every source: How close is it to the original evidence? Who has the authority and expertise? When was it published or updated? Does it support this exact claim? Is it independent of the other sources? Does it include the necessary context and limitations? These questions are more informative than the number of links or a detector score.
Further reading
For a general reference on evaluating sources and checking different media formats, the University of Chicago Press describes Brooke Borel’s The Chicago Guide to Fact-Checking, Second Edition. It is optional background rather than an AI-specific workplace manual. NIST AI 100-4 (2024), NIST’s 2026 evaluation-probe project and NIST AI RMF 1.0 provide additional context on provenance, faithfulness, completeness, sufficiency and context-sensitive review; the evaluation-probe work is described as in development, not as a universally deployed or validated workplace product.
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