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The safest way to review an AI-generated document is to fact-check its important claims against the underlying sources, then correct, qualify, remove, or escalate every claim you cannot support. Fluency is not evidence. A polished draft can contain invented citations, wrong names or dates, arithmetic mistakes, outdated information, and statements that are technically true but misleading without context.
Use a repeatable review: extract the claims that matter, open and assess each source, verify fragile details, check for missing conditions, protect confidential information, and keep a record of what was checked. Human or subject-matter review is essential when the document could affect health, money, safety, rights, policy, or another person’s opportunities.
What an AI-document review must establish
A useful review answers two separate questions:
- Is the statement supported? The cited or authoritative source must exist and support the exact wording, not merely discuss a related subject.
- Is the statement suitable for this use? Even a supported fact may be too old, too general, out of context, or inappropriate for a particular decision, audience, jurisdiction, or risk level.
Review factual assertions rather than trying to judge whether the prose “sounds like AI.” Treat figures, dates, names, event descriptions, causal explanations, recommendations based on facts, quotations, and attributed opinions as claims. Claims can be explicit (“the rate was 12%”) or implied (“this change caused the decline”).
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Generative systems optimize for a coherent response, not for proving every sentence. They can combine details from different sources, infer a relationship that a source never established, attach a real citation to the wrong proposition, or fill a gap with an invented name, date, quotation, statistic, or URL. Grammar and confidence cannot distinguish a supported statement from a fabricated one.
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What the official guidance means for responsibility
The Centers for Medicare & Medicaid Services (CMS) Guidance for Responsible Use of Artificial Intelligence (AI) at CMS, last reviewed August 26, 2025, tells CMS personnel, contractors, and people working on CMS’s behalf to cite sources, document reasoning, validate independently, and remain accountable. It states: “Users are fully responsible for the impact and accuracy of all content produced with GATs at CMS.” That is agency-specific guidance, not a universal legal rule, but it is a useful standard for any organization creating a review process.
CMS also says: “Do not rely solely on AI-generated output for formulating policies or decisions. Always validate information using trustworthy sources, expert consultations, and independent research.” Apply the same principle to reports, customer communications, educational material, internal procedures, and any document that could cause harm if wrong.
Step 1: Make a claim list before editing the prose
Do not begin by polishing sentences. Create an inventory of statements that need evidence. Copy each claim into a review table or spreadsheet and assign it an identifier. Keeping the original wording matters: a later rewrite may quietly make a claim broader or more certain than the evidence allows.
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- Numbers, percentages, prices, measurements, rates, rankings, and calculations.
- Dates, timelines, version numbers, deadlines, and statements about what is current.
- Names, titles, organizations, locations, product or policy descriptions, and quoted language.
- Descriptions of events, legal or regulatory requirements, and claims that one action caused an outcome.
- Recommendations that depend on a factual premise, such as “use this method because it reduces risk.”
- Statements attributed to a person, agency, study, dataset, or publication.
- Comparisons, superlatives, and words such as “always,” “never,” “proves,” “safe,” “best,” or “guarantees.”
- Material omissions: a sentence whose meaning changes when a condition, exception, population, or time period is added.
Separate fact, interpretation, and advice
Label each item as a factual claim, an interpretation, a quotation, or advice. A factual claim can usually be checked directly. An interpretation may require showing how the evidence leads to the conclusion. Advice must be evaluated for the reader’s circumstances and risk, not just for whether its supporting facts are correct. This classification prevents a source that supports a background fact from being treated as proof of a recommendation.
Use a triage column
Prioritize claims by potential harm and by how easily they can change. A typo in a nonessential example is different from an incorrect dosage, eligibility rule, financial figure, security instruction, or deadline.
| Priority | Typical claims | Minimum action |
|---|---|---|
| Critical | Health, safety, legal rights, financial decisions, security, policy, employment, or claims that could cause immediate harm | Verify against authoritative sources and obtain qualified human review before sharing |
| High | Key statistics, conclusions, named events, quotations, deadlines, costs, and recommendations central to the document | Open the source, check exact support and context, and record the result |
| Routine | Background descriptions and low-impact details | Check a reliable source; qualify or remove if support is weak |
Step 2: Open every important source
A citation is a lead, not proof. Follow it to the original document, issuing organization, dataset, official record, or named expert whenever possible. Confirm that the source exists, that its title and date match the document, and that it is actually the source described.
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Prefer the most authoritative available source
- Primary source: the agency rule, court decision, research paper, dataset, technical specification, company filing, or person’s original statement.
- Authoritative secondary source: a professional body or reputable institution that explains primary material and identifies its sources.
- General secondary coverage: useful for discovery, but weaker for a consequential claim when the primary material is available.
Independence matters. A source repeating the document’s own assertion is not independent confirmation. For a changed policy or market figure, compare the issuing organization’s current page with the dated document cited in the draft.
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Check that the citation matches the claim
Read enough surrounding material to identify definitions, scope, sample, date, and exceptions. Ask four questions:
- Does the source make the same claim, or only a related one?
- Does it support the document’s exact number, adjective, and level of certainty?
- Does the source apply to the same population, geography, edition, time period, and conditions?
- Has the document omitted a qualification that changes the practical meaning?
A real source can be misquoted or attached to a claim it never establishes. If a study reports an association, the document should not convert it into proof of causation. If a source describes a pilot, the document should not present the result as a general rule. If a page was updated, record the update date and check whether the draft’s statement remains current.
Handle broken or suspicious citations
If a link is dead, the title cannot be found, the quoted text does not appear, or the named author and date do not match, mark the claim unresolved. Search by the organization and distinctive title rather than trusting a URL copied from the draft. Do not “repair” an invented citation by guessing a similar page. Replace it with a source you can verify, rewrite the sentence to state only what is established, or remove it.
Step 3: Verify fragile details separately
Some errors survive a broad read because they look minor. Check them line by line after the source review.
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Names, titles, and quotations
Confirm spelling, job title, organization, location, and the date at which the title was true. Compare quotations character for character with the original, including whether ellipses or surrounding sentences alter the meaning. If the source gives a paraphrase rather than exact words, do not use quotation marks.
Dates and versions
Distinguish publication date, last-updated date, effective date, and the date an event occurred. A statement such as “the current version” needs a check on the day the document will be shared and, for volatile information, a review date or “as of” qualification. Verify software, standard, policy, and model version numbers against the issuing organization.
Numbers, units, and arithmetic
Recalculate percentages, totals, rates, conversions, and differences yourself. Check whether a number is a count, a percentage, a percentage-point change, an estimate, a range, or a one-time allowance. Preserve the source’s unit and denominator. A figure is not transferable from one population or time period to another merely because the label looks similar.
Scope and geography
Check country, state, language, industry, organization, customer segment, and eligibility conditions. A requirement in one jurisdiction is not automatically a global rule. A result for a particular edition or plan should not be written as a property of every edition or plan.
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Logic and causal language
Highlight words that imply more than the evidence shows: “because,” “therefore,” “leads to,” “prevents,” “ensures,” “proves,” and “will.” Replace them with narrower language when the source supports only correlation, a possibility, or a limited observation.
Step 4: Check context, uncertainty, and omissions
Fact-checking is not only matching sentences to sources. It also asks what a reasonable reader would infer and whether an omitted condition would make that inference wrong.
Use a context checklist
- Who: Who was studied, addressed, regulated, or measured?
- When: What period does the evidence cover, and can the fact change?
- Where: Does the jurisdiction or operating environment match?
- Under what conditions: Were there eligibility rules, test settings, assumptions, or exclusions?
- How certain: Is the evidence definitive, preliminary, disputed, estimated, or based on self-report?
- What is missing: Would a limitation, exception, alternative explanation, or counterexample change the decision?
When credible primary sources disagree, do not select the more convenient statement without explanation. Identify the disagreement, date each source, describe the reason for the difference if established, and state what remains uncertain. If the uncertainty affects a high-impact decision, escalate to a qualified reviewer.
Make uncertainty visible in the sentence
Use precise qualifiers tied to evidence: “the agency reported,” “the study found an association,” “as of [date]” (shown as code if used in a template), or “the source does not establish whether.” Do not add a vague hedge to an otherwise unsupported claim. If no suitable evidence can be found, deletion is safer than decorative uncertainty.
Step 5: Resolve each flagged claim
Every claim in the review table should end in an explicit status. Do not leave “probably right” as a final category.
| Status | When to use it | Editorial action |
|---|---|---|
| Supported | The source exists and directly supports the wording and context | Keep the claim and citation; preserve necessary qualifications |
| Corrected | The source supports a different name, number, date, unit, or wording | Rewrite it, recalculate if needed, and document the correction |
| Qualified | The core point is supported only with limits, uncertainty, or a narrower scope | Add the condition, date, population, or uncertainty in the same passage |
| Removed | No reliable source supports the claim or its impact exceeds the evidence | Delete the statement rather than preserving a guess |
| Unresolved | The claim matters but cannot be verified with available evidence | Hold the document or obtain a suitable subject-matter review |
Escalate when expertise is part of the answer
Ask a qualified subject-matter reviewer when the document interprets law, medicine, finance, safety, security, scientific evidence, or a specialized technical standard. The reviewer should see the original claim, the source passage, your proposed wording, and the unresolved question. Their role is not to approve polished prose blindly; it is to assess suitability, assumptions, and consequences.
Step 6: Protect private and sensitive material during review
Do not paste personal, health, confidential, proprietary, or otherwise restricted material into a public AI service unless you are authorized and the service is approved for that data. Remove unnecessary identifiers, use an approved workspace, and follow your organization’s retention and access rules.
CMS specifically warns its workforce against entering personally identifiable information, protected health information, and sensitive CMS data into publicly accessible AI tools. That instruction applies directly to CMS work; readers should check their own organization’s policy, contracts, data-classification rules, and applicable law before using any external service.
Minimize exposure in a review copy
- Use a redacted or synthetic excerpt when the sensitive text is not needed for the factual question.
- Keep source files and reviewer notes in the approved storage location.
- Limit access to people who need it for the review.
- Record what was shared, with which tool, under which authorization, and when it should be deleted if policy requires deletion.
Step 7: Keep an accountable review trail
Save the version reviewed, the date, the reviewer, the claims checked, the sources consulted, and the changes made. A simple trail makes later correction possible and helps another person reproduce the review.
Recommended review-log fields
| Field | What to record |
|---|---|
| Document identity | Title, version or file name, authoring system if relevant, and review date |
| Claim | Exact sentence or a stable claim identifier |
| Risk | Critical, high, or routine, with a short reason |
| Source | Publisher, title, publication or update date, and location |
| Evidence | The passage, table, data point, or calculation that supports the wording |
| Finding | Supported, corrected, qualified, removed, or unresolved |
| Action and owner | Exact edit, escalation, reviewer, and completion date |
CMS recommends maintaining records of AI use and regularly reviewing documentation to support verification and reproducibility. The exact retention period depends on your organization and the document’s legal or operational status.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.AI detectors and provenance signals cannot fact-check the document
Authorship detection and factual review answer different questions. NIST’s 2024 NIST GenAI (Pilot Study): Text-to-Text Evaluation Overview and Results, published in June 2025, evaluates whether summaries appear AI-generated or human-generated. It explicitly says the pilot “does not address the differentiation between ‘factual’ and ‘non-factual’ semantic content.” A document can be human-written and wrong, or AI-generated and carefully verified.
NIST’s 2024 report on technical approaches to synthetic-content transparency—published November 20, 2024 and updated April 8, 2026—covers provenance, labeling, detection, testing, and auditing. Those mechanisms can help explain origin and handling; they do not establish that a claim is true.
OpenAI’s documentation on provenance signals in OpenAI-generated content, accessed October 13, 2026, describes the limited scope of supported signals. A detected signal is associated with supported OpenAI tools; it does not confirm accuracy, legal ownership, or that 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 remove or prevent a signal. Use provenance as one record about origin, never as a substitute for source checking.
A practical end-to-end review procedure
Use this sequence when you need a repeatable pre-sharing gate.
- Freeze the review version. Save a copy and record the date, author, and intended audience. Do not fact-check a file that is still changing without version control.
- Mark consequential claims. Highlight numbers, names, dates, quotations, causal statements, recommendations, and claims that could change a decision.
- Assign risk. Put health, safety, legal, financial, policy, security, and rights-related claims first.
- Trace sources. Open each citation and confirm the source identity, date, publisher, and relevance.
- Read for exact support. Compare wording, scope, conditions, uncertainty, and exceptions. Capture the supporting passage or calculation in the log.
- Recalculate and cross-check. Verify units, arithmetic, names, dates, versions, and volatile facts independently.
- Review the reader’s likely inference. Look for omitted context, overstated causality, false precision, and language that implies certainty.
- Resolve claims. Correct, qualify, remove, or escalate each item; never leave unresolved high-risk claims hidden in fluent prose.
- Run a privacy check. Remove unauthorized personal or confidential information and confirm the sharing channel is approved.
- Obtain specialist sign-off where needed. Provide the reviewer with source passages and unresolved questions, not merely the final draft.
- Perform a final diff. Recheck claims changed after the first pass. A small edit can alter a number, scope, or level of certainty.
- Archive the trail. Keep the reviewed version, evidence, decisions, and reviewer record according to your policy.
Common failure modes and fixes
| Failure | Why it fails | Fix |
|---|---|---|
| Reading for tone and grammar only | Fluent language can hide unsupported facts | Extract and verify claims individually |
| Checking that a citation exists but not opening it | A citation can be real yet irrelevant, outdated, or misquoted | Read the source passage and context |
| Trusting a search-result snippet | Snippets are truncated and may omit limits or dates | Open the full primary or authoritative source |
| Using one secondary article as confirmation | It may repeat the same error or lack independence | Prefer the issuing organization, original study, record, or dataset |
| Assuming a detector proves truth | Detection concerns origin, not factuality | Perform source-led verification |
| Assuming no provenance signal proves human authorship | Signals can be absent after editing, conversion, or unsupported formats | Treat absence as inconclusive |
| Leaving an invented quotation because it “sounds right” | Attribution errors can damage people and organizations | Verify exact wording or remove quotation marks and attribution |
| Silently broadening a source | A local, dated, conditional result becomes a universal claim | Carry the source’s population, date, conditions, and uncertainty into the sentence |
| Fixing a fact without updating dependent text | Tables, totals, summaries, and conclusions may still contain the old value | Search for every occurrence and rerun calculations |
| Sharing before resolving high-risk uncertainty | Readers may act before a correction is possible | Hold the document or obtain qualified review |
How to decide whether the document is ready to share
Share only when every consequential claim is supported or clearly qualified, every citation is traceable, fragile details have been checked, and the document’s intended use is within the evidence. A document may be ready with a small number of openly labeled uncertainties; it is not ready when uncertainty is hidden behind confident wording.
- Ready: high-impact claims have direct support, calculations and quotations are checked, context is visible, and required specialist review is complete.
- Ready with conditions: the document states its date, scope, assumptions, and unresolved limitations, and the audience can act safely within those limits.
- Hold: a critical claim lacks a reliable source, a citation cannot be verified, private data was exposed, or the conclusion exceeds the evidence.
NIST AI RMF 1.0, published January 26, 2023, is a voluntary, use-case-agnostic framework for managing AI risks and promoting trustworthy use. It is useful for designing organizational controls, but it is not a document-level fact-checking checklist. For the document itself, the claim-and-source workflow remains the practical control.
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