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Ask the AI to separate facts from interpretation, attach a source to every consequential factual claim, and flag anything it cannot support. Then open each source yourself and compare the passage with the claim before you edit for style. A citation is a lead to evidence, not proof that the claim is true. The U.S. National Institute of Standards and Technology (NIST) warns that generative AI can confidently present false content, and that generated citations can appear to justify an answer while misleading the reader.
What NIST says about the problem
NIST’s Generative Artificial Intelligence Profile (NIST AI 600-1, published July 26, 2024) uses the term “confabulation” for this failure. In its words, “Confabulation” refers to “a phenomenon in which GAI systems generate and confidently present erroneous or false content in response to prompts.” Writers often call the same behavior hallucination or fabrication. The confident tone is the hazard: a wrong date, invented quotation, or made-up study reads exactly like a correct one.
The same profile flags two specific risks for anyone who writes with AI help:
- The false content may be a fact, a number, a name, or a whole argument.
- A citation can itself be fabricated or misleading. It may name a document that does not exist, or point to a real document that does not contain the claim.
Traceable output makes review practical. It does not make the output true. Your job is to inspect the evidence behind each claim, not to trust the presence of a footnote.
Step 1: Write the request so the claims can be sorted
A request that asks for “a well-sourced article” produces blended prose in which facts, opinions, and guesses are hard to tell apart. Ask for the answer in a structure you can audit. The template below is a practical editorial method, not a wording NIST publishes; adjust it to your subject.
Answer the question below, then follow these rules.
1. List every factual claim as a numbered item labelled FACT.
2. Put explanation, recommendation, or your own inference under a separate heading labelled INFERENCE.
3. For each FACT, give the publisher, document title, publication or revision date, and the section or page where the claim appears.
4. If you cannot name a source you are confident exists, write NO SOURCE FOUND beside the claim. Do not invent a source.
5. Mark any date, figure, or quotation you are not certain of as UNCERTAIN.
6. State the date of your most recent information and say which facts may have changed since then.
Question: [your question, with the context in which you will use the answer]The labels do not make the answer correct. They make it easier to find the exact sentences that need testing, and they give the model an explicit alternative to inventing a reference.
Step 2: Confirm that each source exists
- Search for the document title on the publisher’s own website. Do not rely on the copy of the citation the AI gave you, and do not ask the same chat to confirm it.
- Check that the publisher, author, and date match the citation. A real title with a changed year or a different organisation is still an error.
- If the citation includes a link, open it. A 404 page, a generic home page, or a page that does not mention the title means the citation is unverified.
- Check the version. Guidance documents are revised, and a passage that appeared in an earlier edition may be gone from the current one.
Step 3: Confirm that the source supports the exact claim
A real source can still fail to support the claim attached to it. Read the passage, not just the title, and compare the scope, the strength of the wording, and the conditions.
| Mismatch | What it looks like | What to do |
|---|---|---|
| Right topic, wrong claim | The document covers the subject, but the specific number or statement is not in it | Mark the claim unsupported and remove it or find a source that contains it |
| Scope drift | The source describes one region, edition, or year, and the answer generalises it | Restore the original scope in your wording |
| Strength drift | The source says “may” or “recommends,” and the answer says “requires” or “will” | Use the source’s own verb |
| Outdated version | The passage has been revised or superseded | Find the current version and note its date |
| Secondary citation | The answer cites an article that itself cites a primary source | Go to the primary source and check that it says what the article says |
Strength drift is common in policy writing. Suppose an answer says that NIST requires organisations to use its AI Risk Management Framework. NIST describes that framework as voluntary guidance, so the sentence needs to be rewritten, even though the framework is real and the topic is correct.
Step 4: Match the level of review to the stakes
Not every AI answer needs the same scrutiny. The table below is editorial guidance for choosing a level of review. It is not a ranking published by NIST.
| Use | Stakes | Minimum check | Domain expert needed? |
|---|---|---|---|
| Brainstorming or background reading | Low | Spot-check the key facts and names | Usually no |
| Blog post, newsletter, or report | Medium | Verify every number, name, date, and quotation against the primary source | Only for technical or specialist claims |
| Commands, configuration, or technical documentation | High | Check against the vendor or project documentation, and test in a non-production environment before relying on it | Often, for system-specific work |
| Medical, legal, financial, or safety decisions | High | Verify against authoritative sources and have a qualified professional review the conclusion | Yes |
When the AI cannot give you a checkable source
Some answers will arrive with references that look plausible but do not survive checking. Treat these signs as a stop, not a puzzle to solve by asking the model for more references:
- The title is specific but returns no results on the publisher’s site or in a standard search.
- A quotation has no speaker, document, or page reference.
- A statistic is given without the organisation that produced it, the year, or the population it covers.
- A link opens to a page that does not contain the title or claim.
The recovery path is to ask the model to restate the answer with NO SOURCE FOUND on every unverified claim, then rebuild the important points from primary sources you locate yourself. Delete any claim you cannot rebuild.
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Reference points and their current status
- NIST AI 600-1, Generative Artificial Intelligence Profile (NIST, published July 26, 2024). This is the source for the confabulation definition and the warning about misleading citations.
- AI Risk Management Framework 1.0 (NIST, released January 26, 2023). NIST describes it as voluntary guidance for incorporating trustworthiness into the design, development, use, and evaluation of AI systems. NIST says the framework is being revised, so check its official page for the current version before you cite it.
- NIST AI Resource Center. It provides resources for testing, evaluation, verification, and validation under the AI Risk Management Framework.
The framework lists trustworthiness characteristics, including validity and reliability, accountability and transparency, and explainability and interpretability. These are useful lenses when you ask whether an answer is checkable. The framework is not binding regulation, and it does not guarantee that any particular AI output is trustworthy.
Best Value
NIST’s material on documented test, evaluation, verification, and validation processes points in the same direction as this checklist: keep a written record of what was checked, against which source, and with what result.
Work through the four steps in order each time: request labelled claims, confirm sources exist, confirm they support the exact wording, and match your review to the stakes. Making the request checkable first is what makes any later polishing trustworthy.
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