Top tips: When “sounds right” isn’t right

Top Tips is a weekly column where we highlight what’s trending in the tech world today and list ways to explore these trends. This week, we’re looking at why convincing AI answers can still be wrong and how to catch them before they slip through.

AI doesn’t fail the way it used to. It doesn’t give obviously wrong answers. It gives answers that are just right enough to trust.

And that’s exactly why we stop questioning it.

It fits into our workflow so easily. You ask, it responds. You skim, it sounds right.

It gets the job done, and most of the time, that’s enough.

But “most of the time” is where the problem hides.

Because the risk isn’t bad answers... it’s convincing ones.

Here’s how to catch them before they catch you.

Don’t trust the tone, check the substance

AI is very good at sounding confident. Clean sentences, structured logic, a tone that feels certain. It’s like reading something that looks official, even when it isn’t.

But tone can be misleading.

Think of it like a well-dressed presentation. Just because it looks polished doesn’t mean the numbers inside are right. The same applies here. A strong answer isn’t defined by how it sounds, but by how it holds up under scrutiny.

If something feels too smooth, pause and ask yourself: "Does this actually make sense, or does it just sound like it does?"

Cross-check what actually matters

Not every answer needs verification. But some do: Numbers, statistics, sources, recommendations—anything that feeds into a decision should never be taken at face value. These are the parts where almost right can quietly become a real problem.

AI often pulls patterns together in a way that feels complete. But sometimes, the details presented to you are estimations, simplifications, or slightly off. And when those details matter, small gaps can lead to big consequences.

A quick check can save more time than fixing something later.

Look for what’s missing

Convincing answers tend to feel complete. That’s what makes them easy to accept.

But completeness can be an illusion. Often, what’s missing is more important than what’s included.

Edge cases, exceptions, alternative perspectives, or the uncomfortable “what if” that wasn’t addressed.

It’s like reading only one side of a conversation and assuming you’ve understood the whole story.

Maybe slow it down for a second and think about "what hasn’t been considered here?" or "is there another way to look at this?" Giving time for extra consideration is usually when the gaps start to show.

Use AI as a starting point, not the final word

The easiest trap is letting AI do the thinking and the deciding.

It’s fast, efficient, and removes friction. And on a busy day, that’s exactly what we want.

A better approach is to treat every response from a GenAI as a first draft rather than a finished outcome. Build on it, question it, reshape it so it aligns with what you actually need. That small shift keeps you involved in the thinking instead of stepping away from it.

The difference is easy to miss, but it matters. One keeps you in control of the decision; the other quietly hands it over.