How to Check an AI Answer When You Are Not the Expert
You cannot verify what you do not know. Five checks that work anyway, and the one question that catches most bad answers.
The advice "always verify AI output" is correct and nearly useless on its own. If you already knew enough to verify the answer, you would not have needed to ask. It gets harder still when the output is an action rather than a paragraph, which is the case with Magic Pointer on Googlebook.
So here are checks that work from outside the subject. They are built around the failure modes in what AI is actually bad at: confident, specific and fluent whether or not it is right. How much checking an answer needs depends on the kind of question it came from, which is sorted out in which questions AI answers reliably.
Doing this reliably, at volume and against a written rubric, is also literally a paid contract role at companies training these models — covered in AI tutor jobs if that side of it interests you.
1. Ask for the source, then go look at it
Not "does a source exist" — go open it. Invented citations are the single most common failure, and they are easy to catch because the check costs thirty seconds. Asking for sources up front is standard vendor advice for exactly this reason. If the tool cannot produce a source you can open, treat the claim as unverified regardless of how reasonable it sounds. Tax and regulatory research is where this bites hardest, which is why how to use AI as an accountant treats every citation as unverified until the primary source is open.
2. Ask the same question in a fresh session
Open a new conversation, phrase it differently, and compare. Consistent answers are not proof of correctness, but an answer that changes materially between attempts is a strong signal that the model is guessing. Benchmark results tell you how a model does on average, not how it did on your question. It guesses because it generates rather than looks up, which is the first word of what GPT stands for.
3. Ask it to argue against itself
"What is the strongest case that this answer is wrong?" This works better than asking "are you sure", which usually just produces reassurance. What you are looking for is whether the objections it raises are ones you can evaluate — often one of them is obviously relevant to your situation.
4. Check the part you do know
Most answers contain something inside your competence — a detail about your own company, a fact about your industry, a step in a process you have done. Check that part carefully. Accuracy there is weak evidence for the rest; an error there is strong evidence against it. This is the mechanism behind a finding in will AI replace accountants: the people who gained most from the tools were the ones best placed to tell when the output was wrong.
5. Notice how specific it got
Vague answers are usually safe and usually less useful. Very specific ones — a percentage, a date, a named study, an exact clause — are where fabrication lives. Specificity is the flag, not the credential.
The question that catches most of it
What would have to be true for this answer to be wrong?
Ask it of yourself, not the model. It forces you to name the assumptions the answer rests on, and assumptions are checkable even when conclusions are not. "This assumes our data is monthly" is something you can confirm in ten seconds, and it is where a surprising number of confident, wrong answers come apart. If you are building something that answers the same question repeatedly, those assumptions become a test set — the step how to create an AI model says everyone skips.
When to stop and ask a person
If the answer is going into something regulated, medical, legal, financial, or irreversible, none of the above is sufficient — that is the territory NIST’s AI Risk Management Framework exists for. The same care applies before you let an agent act for you — whether Meta Muse is safe walks through the permissions worth checking. Those are the cases where the cost of being wrong is not distributed evenly across attempts, and the right move is a human who is accountable for the answer.
Coursium teaches this directly: asking the data a question, testing your own answer, and knowing when you have actually got one. Stay ahead of AI by learning the tools on your phone.