Can AI check its own work?

Not reliably. When you ask an AI model to check its own answer, it usually approves it, because the same gap in knowledge that produced the error also prevents the model from seeing it. Self-review catches formatting and obvious slips, but not confident hallucinations, which are exactly the errors that matter. Real checking has to come from outside the model: a different model that does not share the first one's blind spot, or a human verifying against a real source. An AI grading its own homework gives itself an A almost every time.

By Jason Santiago, author of Let the AI Be Smart. As of 2026-07-06.

Why self-checking fails

A model's mistake is not a typo it can proofread away. A hallucination happens because the model lacks the real information and fills the gap with something plausible. Ask that same model to check the claim, and it consults the same missing knowledge and the same plausible fiction, and concludes it looks right. The blind spot that caused the error is the blind spot that hides it.

What self-review does catch

It is not useless. Asking a model to re-read its answer will catch contradictions, format problems, and math it can recompute. Those are worth doing. Just do not mistake them for verification of the facts.

What actually checks the work

Checking has to come from a source that does not share the error:

A different model. Paste the answer into another AI and ask it to find the weakest claim and say why. A fresh model flags hallucinations the original could not see. This is the core of multi-model verification.
A real source. Open the citation the model gave and confirm it says what the model claims. If you cannot open it, it does not count.
A human. You make the final call on anything that matters.

The disciplined version of "have something that did not write it check it" is the Council Method, taught in Let the AI Be Smart.