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1Fix the driver behind crashes, sound loss and screen glitches2Repair Windows errors before they cause bigger problems3Scan for outdated or missing drivers - takes under a minuteA fluent AI answer tells you nothing about whether it is true. The practical fix is to shape your request so that every consequential factual claim points to a source you can open, and then to check those sources before you spend time polishing the wording. This guide explains how to set up that request, how to run the check, and where the limits lie.
Why a polished answer is not evidence
The National Institute of Standards and Technology (NIST) describes a failure mode it calls “confabulation.” In its Generative Artificial Intelligence Profile, NIST, published July 26, 2024 as NIST AI 600-1, defines confabulation as a phenomenon in which generative AI systems produce and confidently present erroneous or false content in response to prompts. Colloquially, people call the same problem hallucination or fabrication. The confident tone is the risk: wrong content often reads exactly like right content.
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The same profile warns that generated citations can be presented as justification for an answer while misleading the reader. That means a reference list can make an answer look more reliable than it is. A checkable answer therefore has two properties: each claim is labeled as fact, interpretation, or recommendation, and each fact comes with a source you can locate and test.
What to put in the request
Most people ask for an answer and only later wonder where it came from. Put the checking rules into the request itself. A prompt that works across most general-purpose AI tools looks like this:
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I need an explanation of [topic] for [audience/use].
Separate your response into three labeled parts:
1. Factual claims: statements that can be checked against a source.
2. Explanation or interpretation: your reasoning, labeled as such.
3. Recommendations: what you suggest I do.
For every factual claim, give a source with its title, publisher, date, and a direct link if one exists.
If you cannot identify a source for a claim, write "No source found" next to it.
Do not invent titles, authors, dates, or quotations. Mark any uncertainty explicitly.Two details matter. First, ask for the label “No source found” rather than a guess, because the model is more likely to admit a gap when the gap has a defined form. Second, ask for the source’s publisher and date, since those are what you will use to find the document yourself. Requests that omit these fields tend to return citations you cannot locate quickly.
How to check the answer
Work through the answer in this order. Do not begin with style editing; style can make an unchecked claim look settled.
- Isolate the factual claims. Copy only the sentences in part one of the answer into a separate list. Each item should be a statement that could be proved wrong.
- Locate each source independently. Search the publisher’s own website or a library catalog using the title and date the AI supplied. Do not paste the citation back into the AI tool to confirm it, because that only checks the model against itself.
- Confirm the document is authentic and current. Check that the publisher is the one named, that the date matches, and that the version is the one you need. For standards and frameworks, check whether a newer version has been issued.
- Find the passage that supports the claim. Read the section the claim depends on. A source that discusses the same topic is not enough; it has to say what the claim says.
- Record the result. Mark each claim as supported, partly supported, unsupported, or contradicted, and note the page or section where you found the evidence.
- Repair or remove the rest. Rewrite any partly supported claim so it matches what the source says, and delete unsupported claims unless you can find support for them.
Only after this pass should you edit for clarity and tone. The edits should not change any claim that survived the check.
Which claims need the most scrutiny
Not every sentence deserves the same effort. Sort claims by how easily they can be wrong and how much damage an error would do.
| Claim type | Typical example | What to check | Warning sign |
|---|---|---|---|
| Date, number, or version | “The framework was released in 2023.” | The exact figure in the publisher’s own document | A figure with no source, or a source that does not contain it |
| Named document or author | “NIST AI 600-1 addresses confabulation.” | That the document exists at the stated publisher and covers the stated topic | A title that returns no result in the publisher’s catalog |
| Quotation | A sentence in quotation marks attributed to an organization | The exact wording, word for word, in the original | Paraphrase presented as a direct quote |
| Causal or comparative claim | “This method reduces errors.” | Whether the source reports a test, its conditions, and its scope | No test described, or results stated more broadly than the source allows |
| Recommendation | “Use two reviewers for legal text.” | Whether it is labeled as advice, not as established fact | Advice presented as a sourced requirement |
When a source fails the check
The result of a failed check usually falls into one of three branches. Handle each one differently.
- The document cannot be found. Treat the citation as fabricated until proven otherwise. Remove the claim or replace it with a source you have located yourself.
- The document exists but does not say what the claim says. The model has attached a real source to a claim it does not support. Correct the claim to match the document, or drop it.
- The document exists and supports part of the claim. Narrow the claim to the part the source backs. Qualify the scope, such as the region, edition, or date the source covers.
If several claims in one answer fail, do not try to fix them one by one with follow-up prompts. Re-check the whole answer, because the same weakness is likely to appear elsewhere.
How much review a given use needs
The depth of checking should follow the stakes. The table below is an editorial guide, not a formal NIST rating.
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Fix the driver behind crashes, sound loss and screen glitchesFind Drivers →Repair Windows errors before they cause bigger problemsFix Now →Scan for outdated or missing drivers - takes under a minuteDriver Scan →| Use | Minimum check | When to add a subject expert |
|---|---|---|
| Brainstorming or outlining | Confirm any named source before relying on it | Not usually needed |
| Published article or blog post | Open every factual source and verify each figure, date, and quotation | When the topic involves health, law, finance, or safety |
| Internal business or policy document | Full claim-by-claim check, with records of what was verified | Before anyone acts on a recommendation |
| Decisions with legal, medical, or financial consequences | Full check plus independent confirmation from primary sources | Always, with a qualified professional reviewing the final conclusions |
What the official guidance does and does not establish
NIST’s AI Risk Management Framework 1.0, released January 26, 2023, is voluntary guidance for building trustworthiness into the design, development, use, and evaluation of AI systems. NIST says a revision of that framework is underway, so check NIST’s website for the current version before you cite it. The framework is not a binding regulation, and it does not guarantee that any given output is accurate.
Best Value
The framework lists trustworthiness characteristics that include validity and reliability, accountability and transparency, and explainability and interpretability. These give you useful questions to ask of an answer: Is the claim valid for this context? Can someone trace where it came from? Can the reasoning be explained? NIST’s AI Resource Center also supports testing, evaluation, verification, and validation (TEVV) work under the framework, which is the broader process that a single-answer check fits into.
The steps in this guide are an editorial adaptation of that guidance, not a verbatim NIST procedure. NIST does not publish this exact prompt or checklist. What it does establish is the underlying point: an AI system can produce confident false content and misleading citations, so a human has to inspect the evidence before the output is relied on.
A checkable request does not make an answer true. It makes the answer possible to test, and that test is what separates a usable draft from a convincing error.
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