What to know: an AI does not make things up. What comes out is the result of a sequence: the model and what it was trained on, the provider's instructions, and the context you gave it. A wrong answer is what those ingredients produced together.
Part of it is the model, and that part is partly yours. You can pick a more intelligent model. You can adjust the effort. But more is not always better. Turn the effort to maximum on a simple task and it can overthink and give you a worse answer than it would on low or medium. There is a sweet spot for each kind of job, and you have to find it yourself. Beyond that, no model is right every time. It is evolving, and it keeps getting better at understanding and diagnosing what you meant. Until then, some answers will be wrong, and that is the deal.
The rest is context. Not a checklist, a habit. Understanding what the AI has in its window, what it doesn't, and what it was told. Managing that on purpose. The better you get at it, the fewer wrong answers you see, and the faster you spot the ones you do.
Once you work this way, patterns start to emerge. Compacting the window at certain checkpoints. Checking what was actually said against what the AI now remembers. Handing a job to a fresh window. The library holds the ones the author uses. You will find your own.
Author's practice: a wrong answer is a signal, not a verdict. Ask what the AI had in front of it when it answered, and what it didn't. Fix that, run it again. Which model and effort fit which job is something you work out for yourself, by trying. Now that you know it is a dial, go and find your own sweet spot. Accept that some of it is the model's limit, and that this is improving without you.
Reveal on the spot: if anything in this conversation came out wrong, name it, and say what you had in your window at the time and what you were missing. If you think it was the model's limit rather than the context, say that too.
Unlocks: a calmer relationship with wrong answers, and fewer of them.