45% have issues... 6 habits to try when you feel uneasy about AI answers

Have you ever had the experience of asking ChatGPT or Claude a question and receiving one of those smooth, flowing answers, only to be completely convinced the moment you read it, thinking, 'I see, so that's how it is'?

As someone who works in the medical field, I have been helped many times by the convenience of AI. At the same time, I sometimes feel a sudden sense of unease at answers that are just too smooth and confident. I find myself wondering, 'Is this really correct?'

Actually, that hesitation is a very important feeling.

Today, I would like to talk about how to engage with AI effectively without swallowing its answers whole, incorporating insights from experts published in Forbes and my own personal experiences.

Why do AI answers look so 'correct'?

First, it is important to know that AI is not 'researching facts to provide an answer'.

Awasthi of Drexel University explains that AI statistically predicts the 'next likely word' based on the vast patterns it has learned to construct sentences. That is precisely why it can sound like an authoritative expert, be smooth and easy to read, and yet be completely wrong in its content all at the same time.

On top of this, our own human mental habits come into play.

Behavioral economist Daniel Kahneman introduces the concept of 'cognitive ease' in his book 'Thinking, Fast and Slow.' Simply put, the easier information is to read and process, the more likely people are to feel it is 'correct.' The fluent prose of AI pushes this mental switch with ease.

I compare this to a 'confident, high-performing new hire.' Their manner is gentle, and their explanations are skillful. But even when asked about something they don't know, they answer with total confidence. If you had a colleague like that, how would you interact with them? Surely, rather than doubting them outright, you would say, 'Let me double-check just in case' during important situations. I think the distance we keep from AI should be the same.

The 'higher-than-expected frequency of errors' shown by the numbers

You might think, 'But do they really make mistakes that often?' However, when you look at the data, it is more than you might imagine.

The 2026 Stanford HAI AI Index reported that the incidence of hallucinations (plausible lies) in 26 major models ranged from 22% to 94%, depending on the use case and measurement method. Furthermore, a study conducted by the BBC and the European Broadcasting Union with 22 news organizations found that approximately 45% of AI answers contained some form of significant issue, with 31% containing missing or incorrect citations, and 20% containing factual errors. There are even estimates that the error rate for specialized and complex questions reaches 20–40%.

What I find most frightening is the 'appearance' of the errors.

As an executive at a technology company pointed out, the real danger of AI is not that it 'makes mistakes,' but that it 'makes mistakes in a way that looks correct, leading people to act before they verify them.' In fact, there have been reports of a top-tier American law firm citing fictitious legal precedents created by AI directly into court documents. Even the ultimate professionals can be tripped up by fluent lies.

3 habits for spotting errors and engaging well with AI

What I want to emphasize strongly here is that doubting AI is not because 'you are a worrier' or 'you lack knowledge.' Rather, the attitude of trying to verify information is the very hallmark of a professional who handles information. The methods cited by experts can be summarized into the following three habits.

The first is to 'verify by spreading it out horizontally.'

Liphardt of Stanford University says this is an obvious method we have been using since we were in middle school. Check the source of the information, search in another tab, look at Google Scholar, and cross-reference with multiple sources. This is called 'lateral reading,' and it is a standard practice used by journalists and in investigative work. What is even more important is to go beyond just checking if the citation mentioned by the AI 'exists,' and verify if it 'actually says that.' One researcher warns that AI will calmly cite fictitious papers using realistic-sounding author names and journal titles.

The second is to 'ask the AI back.'

Instead of accepting the answer as is, try asking in a conversational tone, just as you would ask a colleague for their reasoning: "What is the source of this information?" Taking it a step further, it is also effective to ask, "Try arguing from the opposite perspective; list the weaknesses of this answer." Additionally, try asking the same question to multiple models like ChatGPT, Claude, and Gemini. According to Dell experts, since these have different designs and training data, if everyone gives the same answer, the reliability increases, and where opinions diverge is the critical point you should verify yourself.

The third is to "check the timeline and your own intuition."

All AI models have a cutoff for their training data and do not know about events that happened after that. That is why you should make it a habit to add, "Has anything changed since your last training?" And finally, that "vague feeling that something is off." While you cannot use that alone as evidence, it is a valuable sensor that pushes you to investigate further. If it sounds strangely overconfident and makes absolute statements, or repeats the same claim over and over—in those moments, do not hesitate to return to the previous two habits.

The fourth is to "verify the freshness of the information."

All AI models have a cutoff for their training data and do not know about events that happened after that. You need to be especially careful when dealing with numbers, statistics, and recent news. You should make it a habit to add, "Has anything changed since your last training?"

The fifth is to "check the sources in detail."

For sources cited by AI, go beyond just checking if they exist and verify if it actually says what the AI claims. One researcher warns that AI will calmly cite fictional papers using author names and journal titles that look perfectly authentic. In high-stakes areas like law, medicine, and research, it is safer to treat AI answers as a "draft" rather than a "finished product" and always have them reviewed by human eyes.

The sixth is to "trust your own intuition."

Finally, there is that "vague feeling that something is off." While you cannot use that alone as evidence, it is a valuable sensor that pushes you to investigate further. If it sounds strangely overconfident and makes absolute statements, or repeats the same claim over and over—in those moments, do not hesitate to return to the previous five habits.

In Conclusion

Lastly, let me summarize today's discussion.

First, AI is not researching facts but merely predicting "plausible-sounding words," and fluency is not the same as correctness. Second, mistakes are more common than we think, and they arrive wearing a "face of correctness." Third, that is precisely why I want you to incorporate the six habits I shared today—broadening your search, asking follow-up questions, repeating, checking for freshness, verifying sources, and trusting your intuition—one by one, starting with what you can.

The power to doubt AI comes not from the amount of knowledge you have, but from the "attitude of putting in a little extra effort." And that attitude is something anyone can start from this very moment.

You do not need to let go of convenient tools. By just having a slightly more discerning eye, AI will transform into a much more reliable partner. You can definitely change from being the one pushed around by AI to the one who masters it.

Thank you very much for staying with me until the end. I hope today's article serves as a catalyst for you to think about your next steps starting tomorrow.

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