How to Check an AI Answer When You Are Not the Expert

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.

So here are checks that work from outside the subject.

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. If the tool cannot produce a source you can open, treat the claim as unverified regardless of how reasonable it sounds.

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.

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.

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.

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. 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.