You can fact-check AI answers before acting by asking for sources, opening them, and checking the exact claim you plan to rely on. The Canadian Centre for Cyber Security’s generative AI guidance advises reviewing generated content against credible sources and says outputs can be incorrect, may not make sense, may not consider every relevant factor, and can be biased. A confident tone is not proof: NIST’s generative AI profile says people may believe false content because a response sounds confident.
Start with the exact claim
Before you inspect a long reply, write down the claim you plan to use and the action it could affect. That keeps your check tied to the next step instead of every polished sentence.
If the claim concerns a date, payment, legal rule, or immediate safety, copy the exact wording and its qualifiers before moving on. Words such as only, usually, may, and except can change what the answer means.
Ask yourself:
- What exactly is being claimed?
- Which source supports it?
- What would change if it were wrong?
If the reply bundles several claims, check them one at a time. A useful habit is to keep the claim and the action separate: first decide whether the claim is supported, then decide what to do. Keep the original wording visible so you do not check a slightly different question by accident.
Ask for sources
Anthropic’s documentation on reducing hallucinations suggests asking a model to cite quotes and sources for each claim so that the response can be audited. You can make the request concrete:
- Which source supports the important claim?
- What passage in that source supports it?
- Does the source cover the same place, time, and conditions?
- Is there a publication or update date?
If the answer gives only an organisation or publication name, ask for the full link, page title, and date. If a page summarises another document, open the underlying document too.
Keep any quoted passage beside the claim it is meant to support. A citation you cannot open is not yet something you have checked.
Open what it gives you
Open every link yourself. Read the surrounding passage, not just the title or a short summary. Look at who publishes the page, what subject it covers, and whether it addresses the same place and situation as your question.
The UK National Cyber Security Centre’s risk explainer says large language models can give convincing-sounding answers that are only partly correct, especially for niche topics. A correct detail can therefore sit beside an incorrect one, so agreeing with one sentence does not clear the whole reply.
For a claim that matters, compare it with another credible source. If the sources disagree, note differences in wording, date, or coverage instead of choosing whichever answer seems more convenient. A source that answers a nearby question may not answer yours.
Ask again and compare
The Anthropic documentation says that running the same prompt several times and comparing the outputs can help because inconsistencies could indicate hallucinations. In plain terms, a disagreement is a signal to check, not a verdict.
Keep the question and its context the same. Then make a short comparison:
- Note any claim that was added or removed.
- Note changed names, dates, quantities, or conditions.
- Note whether the supporting source also changed.
Check each changed claim against a source. Matching answers are not verification. Agreement can tell you where to look, but it cannot replace evidence. Treat any changed part as something to inspect, not as a vote on which answer must be right.
Check the date and uncertainty
Look for a publication date or a last-updated date. If neither is shown, do not guess one. Check whether the page applies to your country, current version, and exact situation. A page may be genuine without answering your particular question.
A recent date does not tell you whether every sentence still applies. Read the stated limits and exceptions before relying on the page.
The Anthropic documentation also says that explicitly giving the model permission to admit uncertainty can reduce false information. Even orderly reasoning is not proof: NIST says a large language model can provide logical steps for an answer even when that answer is wrong.
As a checking habit, ask the answer to separate supported statements, inferences, and unknowns. Then send each important claim back to a reliable source. If you cannot find support, leave the point unresolved instead of choosing the most likely-sounding version.
Ask a person when it matters
For anything about health, money, law, or safety, ask a qualified person before relying on the answer. The Anthropic documentation says critical information should always be validated, especially for high-stakes decisions.
Bring the original answer, its links, the dates, and your notes. State the decision you are considering and explain what you have already checked. Ask the person to check the assumptions as well as the stated facts.
If sources conflict, describe the conflict rather than asking an AI to settle it by choosing the answer that appears more often. You can use the response to organise questions, but it should not be the final basis for the decision.
What Aiamis says
Aiamis’s How It Works page says Ami can state things that are wrong and tells you to check anything that matters with a reliable source or professional. The same page says that no person reads along or types the replies, and that Ami says it is an AI.
That makes the checking step just as relevant here as anywhere else. Knowing what produced a reply does not verify each claim in it.
Keep the routine short
- Write down the exact claim you may act on.
- Ask for a source and the supporting passage.
- Open the source and compare the claim with a reliable source.
- Ask the same question again and compare the material changes.
- Check the date and what the source covers.
- For health, money, law, or safety, ask a qualified person.
Take it one claim at a time. If the support is thin or the sources still conflict, pause before acting. If you need help now, the Aiamis crisis help page lists people you can reach.
Sources
- Canadian Centre for Cyber Security: Generative artificial intelligence (ITSAP.00.041)
- Anthropic Documentation: Reduce hallucinations
- UK National Cyber Security Centre: ChatGPT and large language models: what's the risk?
- US National Institute of Standards and Technology: Artificial Intelligence Risk Management Framework
- Aiamis: How it works
- Aiamis: Crisis help