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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, trace each important claim to an appropriate source, test the source’s date and context, and record what you verified. Increase human and subject-matter review when an error could affect safety, money, rights, compliance or reputation.
AI-authorship detection is a separate question. A detector score, polished prose or an odd detail cannot prove that a statement is true or false. Verify the claim itself, then take responsibility for the final text your team publishes, sends or uses to make a decision.
What must be checked before anyone relies on generated text?
Generative systems can produce useful drafts for brainstorming, summarising supplied material and improving wording. They are less dependable when asked for definitive facts, contested or politically sensitive topics, legal or policy interpretation, current events, impartial analysis or specialist advice without close human oversight. A fluent answer can contain a wrong number, an invented quotation, a citation that does not exist, or a genuine source that does not support the wording.
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Apply a simple default: if a sentence could change a decision or be challenged by a reader, it needs independent evidence. This includes:
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- Names, dates, locations, prices, measurements and statistics.
- Claims about what a law, regulation, contract, standard or company policy requires.
- Quotations and paraphrases attributed to a person or document.
- Causal explanations, forecasts and statements that something is safe, effective, compliant or “the best.”
- Descriptions of current events, products, incidents, research findings or other time-sensitive matters.
Opinions and recommendations still need honest labelling. You may not be able to prove a preference in the same way as a date, but you should identify whose judgement it is and what evidence informs it.
The claim-level verification workflow
| Step | Action | Record or decision |
|---|---|---|
| 1 | Decide whether AI is suitable for the task. | Intended use, audience and consequence of error. |
| 2 | Split the output into checkable claims. | Claim list classified as fact, opinion, interpretation or recommendation. |
| 3 | Inspect every supplied citation. | Authentic document, issuing body, date and supporting passage. |
| 4 | Find evidence independently. | Primary or authoritative sources and independent corroboration. |
| 5 | Test time and context. | Currentness, jurisdiction, population, period, units and definitions. |
| 6 | Check completeness and wording strength. | Omitted caveats, contrary evidence and an appropriate confidence level. |
| 7 | Escalate by risk. | Subject-matter review or a documented decision to qualify or omit. |
| 8 | Own and audit the final text. | Corrected copy and a compact claim-to-evidence record. |
1. Decide whether AI is suitable for the task
Start by stating what the output will do. A draft of internal meeting questions has a different risk profile from instructions that affect a patient’s treatment, an employee’s rights or a customer’s money. Ask who will rely on the text, whether it will be published or sent outside the organisation, how quickly it will become outdated, and what happens if one sentence is wrong.
Appropriate uses with ordinary review
- Generating alternative headings, outlines or interview questions.
- Rewriting a document whose facts have already been checked.
- Summarising a set of documents while retaining the originals for comparison.
- Turning approved information into plainer language or a different format.
Uses that need strong controls or a different process
- Legal, regulatory, tax, medical, safety, security or financial guidance.
- Decisions about hiring, discipline, eligibility, credit, insurance, access or public benefits.
- Claims about a live incident, election, conflict, market event or newly issued rule.
- Research or technical conclusions where a small change in method or population changes the result.
Do not delegate professional judgement or a high-consequence decision to generated text. AI may help organise evidence, but an accountable person must decide what the evidence means and whether the intended use is allowed.
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Read once for meaning, then read again with a marker or spreadsheet. Put each independently testable assertion on its own line. A paragraph such as “The regulation took effect in March, covers contractors and carries a $10,000 penalty” contains at least three claims: an effective date, a scope definition and a monetary consequence.
What to mark
- Factual assertions and comparisons.
- People, organisations, places, dates and event descriptions.
- Numbers, percentages, ranges, totals and calculations.
- Quoted words and close paraphrases.
- Claims that one action caused an outcome or that a product is safe or effective.
- Statements about law, policy, procedure, standards or current events.
Separate evidence from interpretation
Label “The agency reported 42 incidents” differently from “The agency’s controls are failing.” The first can be checked against a report; the second is an interpretation that may require several indicators and a stated method. Mark recommendations and opinions so they are not accidentally presented as objective facts.
Break compound sentences into the smallest useful units without stripping away necessary qualifiers. “A 2025 survey of 600 UK employees found 38% preferred hybrid work” requires checks for the survey organisation, year, sample, country, question wording and the meaning of “preferred.”
3. Inspect every citation yourself
Never treat a citation displayed by an AI system as proof. Open the link or locate the document through the issuing organisation. Confirm that the document is genuine, that the named author or body exists, and that the publication or update date fits the claim.
Use a passage-level test
- Read the title, author or issuing body and date.
- Find the exact table, paragraph, footnote or quotation that should support the claim.
- Compare the source’s subject, population, geography, period and definitions with the sentence you plan to use.
- Check whether the source states the claim, merely discusses it as a possibility, or reports somebody else’s assertion.
- Record the page, section or stable location so another reviewer can repeat the check.
A real source can still be irrelevant, outdated or misrepresented. A report about one jurisdiction cannot automatically establish a rule elsewhere. A source saying that an intervention is associated with an outcome does not necessarily support a causal sentence. If the passage does not carry the wording’s full scope, rewrite the sentence or find better evidence.
4. Find suitable evidence independently
Search for the underlying record rather than for pages that repeat the generated wording. The best source depends on the claim. Official statistics, primary legislation, government departments, recognised regulators, peer-reviewed research and authoritative institutional briefings are useful categories, not a universal ranking.
| Claim type | Usually suitable first evidence | Checks to add |
|---|---|---|
| Law, regulation or policy | Current legislation, official guidance or the relevant regulator. | Jurisdiction, effective date, amendments, exemptions and definitions. |
| Statistic or research result | Original study, dataset, technical report or official statistical release. | Method, sample, uncertainty, measure, period and version. |
| Company or product fact | Current documentation, filing, contract or statement from the responsible organisation. | Edition, region, plan, eligibility and whether the statement is promotional. |
| Historical or biographical claim | Archival record, primary document or reputable scholarly reference. | Quotation accuracy, translation, date and surrounding context. |
| Current event | Direct statements, official records and reporting with named sources. | Timestamp, location, developing facts and independent confirmation. |
Prefer sources close to the original evidence, but also assess the publisher’s authority and expertise. Compare publication or update dates, direct support, independence and completeness. Ten search results that copy one press release are not ten independent confirmations.
Corroborate without creating an echo chamber
For a material claim, seek a second source that is genuinely independent or that checks a different part of the evidence. For example, pair an agency’s incident count with the underlying dataset or an auditor’s report, not with articles that all quote the agency’s press release. When sources conflict, preserve the disagreement and investigate methods, dates and definitions instead of choosing the most confident wording.
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Many apparent “hallucinations” are really context failures. Before accepting a number or rule, write down the answer to each of these questions:
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- When? What period does the evidence cover, and has the rule, product or situation changed since then?
- Where? Which country, state, regulator, market or facility does it concern?
- Who? What population, customer group, employee category or study sample is included?
- What exactly is measured? Check the definition, denominator, inclusion criteria and whether a percentage is a percentage point or a relative change.
- Which units and currency? Confirm conversions, rounding, tax treatment and time zones.
- What edition or version? A standard, software release, contract or policy may have multiple versions in circulation.
Check quotations against the original speaker or document. Preserve qualifying words such as “may,” “in this sample,” “as of June” and “under these conditions.” Removing one sentence of context can reverse the practical meaning without changing any individual word.
6. Check faithfulness, completeness and sufficiency
NIST’s evaluation-probe work describes three useful questions in exact terms: faithfulness — “does the source actually support the claim?”; completeness — does the output capture the source’s full message; and sufficiency — does the source carry the evidentiary burden for the confidence of the statement?
This project, described by NIST as work in development, is a way to think about review, not a universally deployed or validated workplace product. Apply the questions manually or in your existing review system:
Faithfulness
Compare every important verb and adjective with the source. “Found” is stronger than “suggested”; “causes” is stronger than “is associated with”; “all” is stronger than “some.” Replace unsupported precision with wording the evidence can bear.
Completeness
Look for exclusions, confidence intervals, limitations, competing explanations, exceptions and failed outcomes. A summary that mentions only positive findings may be accurate sentence by sentence yet misleading overall.
Sufficiency
Ask whether one source is enough for the decision. A small observational study may support a cautious description but not a safety guarantee. A company FAQ may explain a feature but not establish compliance with a legal requirement. Match the strength of the conclusion to the strength and quantity of evidence.
7. Escalate review according to risk
Human review is not a single pass applied equally to every sentence. Create a risk rule before checking so urgency does not lower the standard.
| Risk level | Examples | Minimum control |
|---|---|---|
| Lower | Internal brainstorming, non-factual wording options, background summaries. | Author checks claims and links before circulation. |
| Medium | Customer education, operational instructions, public statistics or product documentation. | Claim record, source review and a second reader for material assertions. |
| High | Legal, medical, safety, financial, employment, security or rights-affecting content. | Qualified subject-matter expert, authoritative primary evidence and documented approval. |
Ask an expert when the material is specialised, contested, sensitive or consequential. The NIST AI Risk Management Framework 1.0 emphasises evaluating findings in relation to intended use and deployment conditions, with subject-matter experts assisting that evaluation. If evidence is unavailable or credible sources conflict, qualify the claim, omit it or state the uncertainty. Never fill the gap with plausible language.
“The best guard against hallucinations from AI is to check everything generated carefully, ideally with an expert.” — Chris Rhodes, Working with AI and spotting AI-generated text, House of Commons Library, published 19 May 2026.
8. Own the final text and keep an audit trail
After checking, edit the output as if you had written it from scratch. Remove invented citations, correct errors, add material caveats, distinguish fact from opinion and make the intended audience and date clear. The person or team using the text remains responsible for what is published, sent or used in a decision.
A compact claim-to-evidence record
A spreadsheet or review note is enough if it is complete and retrievable. Use one row per material claim:
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| Field | Example entry |
|---|---|
| Claim ID | C-014 |
| Proposed wording | “The rule applies to contractors from 1 March 2026.” |
| Claim type | Policy scope and effective date. |
| Source | Issuing regulator, document title, version and stable location. |
| Supporting passage | Section 2.1 and implementation note, checked 17 October 2026. |
| Context checks | Jurisdiction, contractor definition, exemptions and commencement date. |
| Result | Verified, narrowed, unresolved or rejected. |
| Reviewer | Name, role and review date. |
Keep the original generated output, the edited version and the evidence record under the same document or ticket. This makes later updates faster and shows why a sentence was changed when a policy or fact moves.
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Why AI detectors cannot fact-check the content
Authorship detection asks whether text appears to have been generated or assisted by an AI system. Fact-checking asks whether each claim is supported by evidence. They are different tasks. The House of Commons Library briefing says AI detectors are unreliable and should not be treated as conclusive. NIST evaluations may measure content-authorship detection, but that does not establish factual correctness.
Fluent prose, vague wording, odd details, weak citations and a suspiciously uniform style can justify closer scrutiny. They do not prove that a human or a machine wrote the text, and they do not prove that a specific sentence is false. A human can misstate a fact; an AI system can reproduce a correct quotation. Check the claim either way.
Images, audio, video and provenance claims
When the output includes media or says that a file is authentic, add an origin-and-context check. Metadata, watermarking, labels and provenance systems can offer clues about where a file came from or how it changed. NIST AI 100-4, published 20 November 2024, cautions that transparency about origin or history does not itself guarantee trustworthiness; media can be taken out of context or manipulated after creation.
- Locate the earliest available copy and record its date, account or issuing body.
- Check the surrounding post, transcript, caption and event context.
- Compare with independent recordings, official statements or contemporaneous reporting.
- For an image, inspect details and use reverse-image search to identify earlier uses or a different event.
- Do not treat a missing watermark or metadata as proof that the content is real or fake.
Use provenance signals as another evidence layer, never as a substitute for checking what the media actually depicts or claims to show.
Common failure modes and the corrective action
| Failure | Why it fails | Correction |
|---|---|---|
| Accepting a polished answer at face value | Fluency is not evidence. | Extract and verify each material claim. |
| Searching only the exact generated sentence | Copied wording can create a false consensus. | Search for the underlying record and original data. |
| Checking that a link works and stopping | A live page may be irrelevant, outdated or misquoted. | Read the supporting passage and compare scope. |
| Counting search results as corroboration | Many pages may trace to one source. | Trace provenance and seek independent evidence. |
| Using a detector as a truth test | Authorship and accuracy are separate questions. | Verify claims regardless of detector output. |
| Ignoring small qualifiers | Removing “estimated,” “in this sample” or a date can overstate a result. | Preserve limitations and context in the final wording. |
| Leaving unresolved claims in confident prose | Readers cannot see the evidence gap. | Qualify, omit or explicitly state uncertainty. |
| Failing to assign an accountable reviewer | No one owns the final decision. | Name a reviewer and retain the claim record. |
A reusable pre-publication checklist
- Have you defined the audience, purpose and consequence of an error?
- Have you separated factual claims from opinion, interpretation and recommendation?
- Have you checked every name, date, number, quotation and citation?
- Did you open each source and read the relevant passage rather than trusting the link text?
- Is the source appropriate to the jurisdiction, subject and evidentiary burden?
- Have you checked the publication date, update date, version, population, period, units and definitions?
- Did you look for caveats, exclusions, contrary evidence and omitted context?
- Are any claims stronger than the evidence? If so, have you narrowed the wording?
- Has a qualified person reviewed specialised, contested or high-consequence material?
- Can another reviewer map every important claim to evidence and see who approved it?
- Have you checked media origin and context separately from any provenance signal?
- Would you stand behind the final wording if the generated draft were removed from the record?
Further reading
For a general fact-checking reference, Brooke Borel’s The Chicago Guide to Fact-Checking, Second Edition covers source quality and checks for several media types. It is a broad reference rather than an AI-specific workplace manual. NIST AI 100-4 (20 November 2024), the NIST AI Risk Management Framework 1.0, and the House of Commons Library briefing by Chris Rhodes (19 May 2026) provide additional context on provenance, risk, expert review and AI-generated text.
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