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Use prompts that make an AI answer easier to inspect—not prompts that ask the AI to certify itself. These five copy-and-paste prompts break an answer into claims, expose uncertainty, request evidence, test whether citations support the claims, and give you a verification checklist. You still need to open the sources and judge them yourself.
Why an AI answer needs checking
A fluent, confident answer can still be wrong. OpenAI warns that ChatGPT may produce fabricated quotes, studies, citations, or references, and recommends checking important information by visiting sources directly. Its practical advice is: “Use ChatGPT as a first draft, not a final source.”
A citation is a lead to inspect, not proof. In a 2023 domain-specific study, Guido Zuccon, Bevan Koopman, and Razia Shaik found that 50.6% of the answers in their test were correct or partially correct, while only 14% of suggested references existed. Even when references existed, they often failed to support the claims attributed to them. Those results describe that study’s questions and setup; they are not a current accuracy rate for all models or prompts.
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#1 Best Overall
Five prompts to inspect an answer
Paste the answer you want to check after each prompt, or ask the model to apply the prompts to its previous response. For claims that depend on current or specialized information, enable search if available—but still inspect the linked pages yourself.
1. Separate the answer into claims
Break your answer into the individual factual claims it depends on. Mark which are directly supported by the information I gave you, which are assumptions or inferences, and which you cannot verify.
This helps reveal when a polished paragraph combines facts, interpretation, and missing information. A claim based only on what you provided is not independently verified; it is merely grounded in that input.
2. Surface uncertainty and possible staleness
For each claim, state what could make it wrong or outdated. If you are uncertain, say so plainly rather than filling the gap with a guess.
Rank #2
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Pay particular attention to claims involving dates, policies, product features, prices, laws, or other details that can change. A model may have a knowledge cutoff, and events after that cutoff may be absent unless it uses tools. Search can help with current information, but access to relevant pages can fail because of technical issues, paywalls, or site preferences.
3. Ask for evidence you can open
For each important external claim, give the original or most authoritative source you can identify, with a direct link and the specific passage or data that supports it. If you cannot find one, say that.
Prefer the underlying source—such as an official document, dataset, or study—over a page that merely repeats the claim. Check that the link opens and that the cited passage says what the answer claims it says. A link supplied by the model may be broken, irrelevant, or incorrectly described.
4. Check whether each source supports its claim
Compare each claim with the cited source. Tell me whether the source directly supports it, only partly supports it, or does not support it. Do not treat a citation's existence as proof.
Rank #3
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Look for changes in scope: a result from one population presented as universal, an association described as causation, or a tentative finding stated as settled fact. The source itself may also be outdated or less authoritative than another available source. The model’s assessment can point you to a mismatch, but it is not independent confirmation.
5. Get a prioritized verification checklist
List the claims I should verify myself before acting on or sharing this answer, prioritizing current, numerical, quoted, technical, and consequential claims. For each, say what source would resolve it.
Use the checklist to decide what needs checking first. Verify high-impact claims against primary or otherwise authoritative sources, and confirm quotations in the original text rather than relying on a paraphrase or a search snippet.
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How much checking is enough?
Match the effort to what you plan to do with the answer. For low-stakes brainstorming, you may only need to correct obvious errors and avoid presenting suggestions as facts. Before publishing, sharing factual claims, or making a decision, verify the relevant claims against sources directly. Give extra attention to current facts, numbers, quotations, technical details, and external references.
Search-enabled answers may provide newer information and citations, but a search feature does not guarantee that the model found the right source or that a citation supports the attached claim. Open the source and check it in context. If you cannot establish a consequential claim from a reliable source, do not rely on the model’s confidence as a substitute.
What these prompts can—and cannot—do
These prompts are practical ways to make uncertainty and evidence gaps more visible; they are not experimentally validated guarantees of improved accuracy across current AI models. Asking a model to critique its own answer can surface useful questions, but it is not independent fact-checking. Treat its explanations, citations, and self-assessments as things to inspect.
For organizations managing broader generative-AI risks, NIST’s AI Risk Management Framework is a voluntary framework, not a consumer prompt recipe. Its generative-AI profile, released July 26, 2024, helps organizations identify risks and consider risk-management actions.
The useful outcome is not an AI answer that says it is certain. It is a set of claims you can trace to sources, evaluate, and verify before you rely on them.
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