The best way to handle it is to never take AI answers at face value, especially when the topic actually matters. Treat it more like a quick assistant than a final source. If something feels even slightly off, ask it to explain its reasoning or break it down step by step, because wrong answers usually start to show gaps there. It also helps a lot to add real context in your prompt instead of asking general questions. The more specific the situation, the less confident guessing you get back. And for anything important, a quick cross check with a reliable source or documentation should always be part of the process before acting on it.
The biggest issue with AI isn’t always the wrong answer itself, but the confidence behind it. A lot of people assume that if the response sounds clear and detailed, it must be accurate. I usually try to challenge the answer a bit by asking for sources, examples, or possible exceptions. Sometimes that quickly reveals where the model is making assumptions instead of actually knowing something. It’s a useful tool, but I treat it more like a first draft than a finished answer.