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Before sharing an AI-assisted document, verify its important factual claims against the underlying sources—not the fluency of the prose. Check that each cited source exists, supports the exact statement, and has not been stripped of context. Then review the whole document for omissions and unexpected claims. NIST recommends fact-checking generated information, especially when it comes from multiple or unknown sources.
Start by marking claims that could mislead a reader
Read the document once to identify claims that need evidence. Prioritize anything central to the document’s purpose and anything that could materially affect a reader’s understanding or decision.
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- Dates, figures, names, quotations, and descriptions of what a source says.
- Cause-and-effect statements, comparisons, and recommendations presented as factual.
- Claims that sound unusually precise, surprising, or consequential.
This is a practical way to apply NIST’s recommendation to fact-check generated information, particularly when it draws on multiple or unknown sources. NIST’s Generative AI Profile (NIST AI 600-1) says to “Deploy and document fact-checking techniques to verify the accuracy and veracity of information generated by GAI systems, especially when the information comes from multiple (or unknown) sources.”
Open each source and check what it actually says
Do not treat an AI-generated citation, a search-result snippet, or another summary as proof. Open the cited document. Confirm that it exists, identify who published it and when, and read enough surrounding material to understand its context.
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Then compare the source with the sentence it is meant to support. A source can mention the same subject without substantiating the specific wording. Check whether the evidence supports the claim’s details, date, scope, and level of certainty. For example, a source describing a limited trial would not by itself support a claim about what always happens or what happens for everyone.
NIST’s publication on evaluating machine-generated reports discusses whether citations map claims to source documents and treats claim-source support as part of verifiability. See NIST IR 8498.
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Check for missing context, limits, and counterevidence
A sentence can be accurate as far as it goes and still leave a misleading impression. Look for qualifications that the draft omits: who or what a finding applies to, how it was measured, when it was true, and what limitations the source identifies. Check whether relevant evidence points in a different direction.
Completeness matters alongside accuracy and verifiability. In practice, ask whether a reader would reach a different understanding if they saw the omitted qualification or evidence. If so, add the context or narrow the claim.
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Use stronger evidence for consequential claims
For claims that could affect an important decision, prefer original records and authoritative sources close to the underlying evidence. Evaluate a source by more than its apparent relevance:
- Authority and proximity: Is this an authoritative source, and is it close to the original evidence?
- Direct support: Does it support this exact claim, rather than the general topic?
- Date and scope: Do its date, population, jurisdiction, or other limits match the sentence?
- Context: Are important caveats and counterevidence represented?
- Potential harm: What could happen if the claim is wrong or incomplete?
For medical, legal, financial, or similarly high-consequence documents, general fact-checking is not a substitute for appropriate domain-specific review. NIST advises checking the accuracy and veracity of generated information, particularly when it comes from multiple or unknown sources; its guidance does not guarantee that a checklist will catch every error.
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Review the whole document as a human
After checking individual claims, read the document from beginning to end for reliability in its intended use. Look for unexpected assertions, contradictions between sections, conclusions stronger than the evidence, and gaps that make the account incomplete. A verified citation beside one sentence does not validate the entire document.
The NIST AI RMF Playbook’s Measure guidance recommends human review to track the reliability of generated outputs and to warn users when outputs may be unreliable. Text that sounds human—or passes a text detector—does not establish that its claims are true. NIST’s 2025 text challenge treats distinguishing text by origin and evaluating accurate versus misleading narratives as separate concerns; see the NIST GenAI Challenge evaluation plan.
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Correct, qualify, remove, or hold unsupported claims
For each flagged claim, make its status clear before sharing:
- Keep it when a reliable source supports the precise wording and relevant context is included.
- Correct it when the source shows the draft is wrong.
- Qualify it when the evidence supports a narrower or less certain statement.
- Remove it when it is unsupported and not essential.
- Hold the document when a consequential claim cannot be verified or needs specialist review.
A citation is useful only when the cited document exists and supports the claim attributed to it. The goal is not to make every sentence sound certain; it is to ensure the document’s certainty matches its evidence.
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