Do not share an AI-generated document until a person has checked every important claim against the underlying evidence. A fluent draft can contain invented facts, mismatched citations, wrong names or dates, faulty arithmetic, and missing conditions. The reliable method is a claim-by-claim review: list the statements that matter, open the cited sources, test whether each source supports the exact wording, recheck fragile details, resolve uncertainty, protect sensitive information, and preserve a record of what was checked.
What a proper pre-sharing check must establish
A document is ready to circulate only when its material statements are either supported, clearly qualified, removed, or assigned to an appropriate subject-matter reviewer. “Material” means a reasonable reader could make a decision, spend money, change behavior, assess risk, or judge a person or organization differently because of the statement.
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- Factual accuracy: Names, dates, figures, units, quotations, descriptions of events, and causal explanations match reliable evidence.
- Source fit: A citation leads to a real source and that source is the one the document identifies.
- Support: The source establishes the precise claim, not merely a related topic.
- Context: Conditions, time periods, exceptions, uncertainty, and scope have not been silently omitted.
- Accountability: Someone can show which version was reviewed, which sources were consulted, what changed, and who approved release.
Grammar and polished style are useful editorial qualities, but they are not evidence. Treat a confident sentence as unverified until its support is demonstrated.
Do these 3 things before closing this tab:
1Repair Windows errors before they cause bigger problems2Fix the driver behind crashes, sound loss and screen glitches3Clear out junk files and repair common Windows errorsStep 1: Turn the document into a claim list
Read the document once for meaning, then make a separate inventory of statements that require checking. Do not rely on memory or on the document’s citation formatting. Copy each claim into a review sheet and give it a unique identifier.
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Flag these types of statements
- Numbers, percentages, totals, rates, rankings, prices, measurements, and forecasts.
- Dates, timelines, ages, version numbers, locations, and sequences of events.
- Names of people, organizations, products, laws, agencies, datasets, and technical standards.
- Claims that one event caused another, or that a recommendation will produce a particular result.
- Descriptions of what a policy, contract, study, regulation, software release, or public statement says.
- Quoted words and attributed opinions, including short excerpts that appear harmless.
- Recommendations whose suitability depends on an external fact, such as a security setting, eligibility rule, medical condition, or legal requirement.
Separate factual claims from interpretation. “The report was published in June 2025” is a factual claim. “The report is useful for small teams” is an evaluative judgment that still needs an explained basis, but it should not be presented as a measured fact unless evidence supports that wording.
Use a review table
| ID | Document wording | Claim type | Source named? | Status | Action |
|---|---|---|---|---|---|
| C-01 | Exact sentence from the draft | Figure/date/name/causal/recommendation | Yes or no | Unverified | Open source and test support |
| C-02 | Exact sentence from the draft | Quotation or attribution | Yes or no | Unverified | Check wording, speaker, and context |
Preserve the document’s exact wording in this table. Rewriting a claim before testing it can hide the very error you are trying to find.
Step 2: Open every important source
Follow each citation, footnote, hyperlink, or named reference to the underlying material. Confirm that the page, publication, dataset, transcript, or legal text actually exists and that its title, author, date, and issuing body match the document.
Prefer the most authoritative available source
- Use an original issuing organization, primary document, official dataset, court or legislative record, or named expert’s own publication when available.
- Use a reputable secondary source when it accurately reports primary evidence or when no primary material is available.
- Treat search snippets, unsourced summaries, reposts, social posts, and AI-generated explanations as leads rather than proof.
Independence matters. A company describing its own performance is relevant evidence for what the company claims, but it is not independent confirmation of the result. Record that distinction rather than treating every source as interchangeable.
Check source identity before reading for support
- Does the link resolve to the cited source rather than a home page, search result, or unrelated document?
- Is the document version or edition the one the draft names?
- Was the source published, updated, or withdrawn at a relevant date?
- Is the author or issuing body qualified to make the statement?
- Could the page have changed since the draft was written?
For a volatile fact, record the access date and the relevant version. A current software limit, policy, price, or eligibility rule should not be presented as timeless.
Step 3: Test whether the source supports the exact claim
A citation can be genuine and still be wrong for the sentence attached to it. Read enough surrounding material to determine what the source actually establishes, including headings, footnotes, definitions, methodology, limitations, and the relevant time period.
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Use a support test
- Locate: Find the passage, table, figure, dataset row, or recorded statement that supposedly supports the claim.
- Match: Compare the draft’s subject, number, unit, population, geography, date, and level of certainty with the source.
- Check direction: Make sure the draft has not reversed a relationship, confused correlation with causation, or changed “may” into “will.”
- Check scope: Determine whether a result applies to a particular sample, jurisdiction, product edition, or test condition rather than everyone.
- Record the result: Mark the claim supported, partly supported, contradicted, unresolved, or unsupported, with a short reason.
For example, a source saying that a pilot “evaluates AI-versus-human summary detection” does not establish that the summaries are factually correct. The National Institute of Standards and Technology’s June 2025 text-to-text pilot explicitly says it “does not address the differentiation between ‘factual’ and ‘non-factual’ semantic content.” That limitation must remain attached to any description of what the pilot demonstrates.
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Do not promote a source’s possibility into a promise
Words such as “can,” “may,” “associated with,” “estimated,” and “under these conditions” carry meaning. Preserve them when the evidence uses them. Remove absolute wording unless the source genuinely supports it.
Step 4: Recheck fragile details
Some errors are easy to miss because a sentence looks plausible. Give these details a second, deliberate pass even after the general claim appears supported.
| Detail | What to verify | Common failure |
|---|---|---|
| Names | Spelling, title, affiliation, and identity | Combining two people or assigning a quote to the wrong speaker |
| Dates | Publication date, event date, update date, and time zone | Confusing an announcement with an effective date |
| Numbers | Original unit, denominator, rounding, and calculation | Turning a one-time count into a recurring rate |
| Quotations | Exact wording, speaker, transcript, and omitted context | Paraphrase presented inside quotation marks |
| Versions | Edition, release, jurisdiction, and applicability | Applying an old rule or different edition |
| Comparisons | Same definitions, population, period, and measurement method | Comparing unlike figures as if they were equivalent |
When a number matters, write down who published it, when, for which population or product, and under what conditions. If you cannot carry those qualifications with the number, rewrite the sentence or remove the number.
Recalculate instead of trusting generated arithmetic
Check totals, percentages, unit conversions, averages, growth rates, and date intervals independently. Use the source’s raw values where possible. A calculator can verify arithmetic, but it cannot determine whether the chosen denominator or time period is appropriate.
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Step 5: Find missing context and unresolved uncertainty
Misleading documents often contain individually true sentences arranged to imply more than the evidence supports. Ask what a reasonable reader would need to know to interpret each claim safely.
- What population, geography, organization, or product does this apply to?
- What period was measured, and is the fact likely to change?
- Were there exclusions, assumptions, eligibility conditions, or exceptions?
- Is the evidence observational, experimental, self-reported, modeled, or anecdotal?
- Do credible primary sources disagree?
- Does the document distinguish evidence from prediction, opinion, and recommendation?
When sources disagree, do not select the more convenient result without explanation. State the disagreement, identify why the sources may differ, and describe what remains uncertain. If the disagreement affects a consequential decision, escalate it to a qualified reviewer.
Use calibrated language
Choose wording that matches the evidence: “the agency reports,” “the study found in its sample,” “the available record does not establish,” or “this remains uncertain.” Avoid turning a limited source into an unqualified generalization.
Step 6: Resolve every flagged claim
Each claim should end in one of four dispositions:
- Correct: The wording and citation accurately reflect the evidence.
- Qualify: Keep the claim but add the missing scope, date, condition, uncertainty, or attribution.
- Remove: Delete a claim that is unsupported, misleading, unnecessary, or impossible to verify.
- Escalate: Send a consequential or technically specialized issue to a suitable subject-matter reviewer and record the decision.
Do not leave a sentence marked “probably right.” An unresolved claim is a release blocker when it could affect safety, health, legal rights, finances, reputation, public policy, or access to services.
When expert review is appropriate
Seek qualified review for medical, legal, financial, security, regulatory, scientific, engineering, or policy conclusions; for claims involving vulnerable people; and for decisions with substantial public impact. The reviewer should assess the substance, not merely proofread the prose. Give that person the claim list, sources, and the exact unresolved question.
Step 7: Protect sensitive information during checking
Fact-checking can create a second risk if the draft contains personal, health, confidential, proprietary, or otherwise restricted information. Do not paste such material into a public AI service or external checker unless you have authorization and the service is approved for that data.
Centers for Medicare & Medicaid Services guidance warns its workforce not to enter personally identifiable information, protected health information, or sensitive CMS data into publicly accessible AI tools. That is an agency-specific rule, not a universal law; readers should follow their own organization’s information-security policy, contractual obligations, and applicable privacy requirements.
Minimize exposure while reviewing
- Remove names, account numbers, addresses, case identifiers, and unnecessary excerpts.
- Use an approved internal environment for restricted material.
- Share only the smallest passage needed to resolve a claim.
- Check retention, access, logging, and deletion rules before uploading anything.
- Record who had access to the review copy.
Step 8: Keep an accountable review trail
Save the version reviewed, the date and time, the reviewer, the sources consulted, claim statuses, corrections, and the final approval. A compact log makes later correction possible when a source changes or a reader challenges a statement.
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|---|---|
| Document identity | Title, version, file hash or controlled revision number |
| Review timing | Date, time zone, and whether the source is time-sensitive |
| Claim record | Claim ID, exact wording, source, passage checked, and disposition |
| Changes | Original wording, correction or deletion, and reason |
| Approval | Reviewer name or role and any required subject-matter sign-off |
CMS guidance recommends documenting reasoning, maintaining records of AI use, and regularly reviewing documentation to support verification and reproducibility. Its statement that “Users are fully responsible for the impact and accuracy of all content produced with GATs at CMS” applies to CMS personnel and work performed on its behalf; it illustrates the accountability principle without creating a universal rule for every organization.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Why AI detectors cannot certify factual accuracy
Authorship detection and fact-checking answer different questions. A detector may estimate whether text resembles AI-generated writing; it does not establish that a factual claim is true, that a citation is correctly attached, or that a quotation is authentic.
NIST’s June 2025 text-to-text pilot was designed to distinguish AI-generated summaries from human-generated summaries and expressly excluded factual-versus-non-factual semantic content. Running a detector therefore cannot replace the claim-and-source workflow.
Provenance signals have similarly limited scope. OpenAI’s documentation on provenance signals says a detected signal is associated with supported OpenAI tools; it does not confirm accuracy, legal ownership, or whether content is in context. A missing signal does not prove that AI was not used: unsupported formats, older content, editing, conversion, or stripped metadata can explain its absence.
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How official frameworks fit into the review
CMS responsible-use guidance
The Centers for Medicare & Medicaid Services page, last reviewed August 26, 2025, is written for CMS employees, contractors, and people working on CMS’s behalf. It advises citations, documented reasoning, independent validation, trustworthy sources, expert consultation, and user accountability. Apply it as an authoritative example of operational practice, not as a blanket legal mandate for every reader.
Its direct warning is: “Do not rely solely on AI-generated output for formulating policies or decisions. Always validate information using trustworthy sources, expert consultations, and independent research.”
NIST AI Risk Management Framework
NIST AI RMF 1.0, published January 26, 2023, is a voluntary, use-case-agnostic framework for organizations managing AI risks and promoting trustworthy, responsible use. It can help an organization assign roles, define risk tolerances, and govern review, but it is not a document-level fact-checking checklist. You still need the claim inventory and source checks described above.
A practical release checklist
- Every material factual statement has a source or has been removed.
- Each source exists, is correctly identified, and is appropriate for the claim.
- The source supports the exact wording, including qualifiers and uncertainty.
- Names, dates, units, quotations, versions, and arithmetic have been independently rechecked.
- Claims have the right geography, population, time period, edition, and conditions.
- Conflicting evidence is disclosed or escalated rather than silently resolved.
- Recommendations are separated from facts and reviewed by an appropriate expert when stakes are high.
- Private, personal, health, confidential, and proprietary information has been handled under an approved policy.
- No detector, watermark, or provenance signal is being treated as proof of truth.
- The reviewed version, source log, corrections, reviewer, and approval are preserved.
A compact example of correcting an AI sentence
Draft: “The NIST pilot proves that AI summaries are accurate and can be trusted.”
Review: The source describes a pilot for distinguishing AI-generated from human-generated summaries and says it does not assess factuality. The draft changes a detection task into an accuracy finding.
Release wording: “NIST’s June 2025 pilot evaluated whether summaries could be distinguished by origin; it did not evaluate whether their content was factual.”
The correction does not merely add a citation. It removes the unsupported conclusion and preserves the source’s stated limitation.
What to do when a document cannot be fully verified
Pause sharing if an unresolved claim could materially mislead readers. Replace it with a narrower, supported statement, label it as an unverified assertion only when there is a legitimate reason to retain it, or obtain qualified review. If the document has already circulated, issue a correction that identifies the affected statement, explains the change, and points readers to the corrected version.
The goal is not to make every sentence sound certain. The goal is to make the document’s confidence match its evidence, while leaving a clear path for another person to reproduce the check.
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