Most of the trouble people have with AI comes from one habit: reading the answer as if it were the final word. A couple of small changes fix most of it. Make your own guess before you look, and treat what you get back as a draft. Neither one takes extra time, and together they make the tool both safer and more useful.

Make your own guess first

Before you read the AI’s answer, take a second to ask yourself what you expect. If you are asking whether a clinic takes your insurance, pause and think: what do I already believe is true here? Then read the reply and compare it to your guess.

This tiny step does something powerful. When the answer matches what you expected, you can move ahead with more confidence. When it disagrees with your guess, that gap is a signal. It does not mean you were right and the AI was wrong, or the reverse. It means this is a spot worth checking before you act. Research on people making decisions with an AI helper found that a small step that makes you stop and think for yourself first reduces the chance of going along with a wrong answer just because it sounds sure (Buçinca et al., 2021).

Treat the answer as a draft

Think of an AI answer the way you would think of advice from a knowledgeable neighbor. Your neighbor has been through the system, means well, and can save you real time. Your neighbor can also be out of date, or right about their own case but wrong about yours. You would happily take their help and still confirm anything important before you signed a form or missed a deadline.

An AI answer deserves the same reading. It is a strong first draft: a starting point you can shape, question, and check. When you treat it as a draft, a mistake becomes something you catch during editing instead of something you discover after you acted on it.

A good way to work with a draft is to say it back in your own words before you act on it. Putting the answer through your own understanding often surfaces a gap or a wrong assumption right away. There is a simple routine for this near the end of the guide.

”I am not sure” is a good sign

You might expect a helpful tool to always sound certain. The opposite is true. When an AI says something like “I am not sure” or “this can vary, so please confirm,” it is giving you honest information about where its answer is shaky. A study of people using an AI search tool to answer medical questions found that when the tool expressed its uncertainty in plain words, people relied less on wrong answers and judged the answers more accurately (Kim et al., 2024). Uncertainty is a feature that helps you, so welcome it when you see it, and be a little more careful with answers that sound sure about specific, local, or changing facts.

Trust that goes both ways

Using AI wisely is about matching your trust to the situation, and that runs in two directions.

In one direction, do not believe everything. A fluent, confident answer can still be wrong, especially about phone numbers, addresses, costs, and eligibility rules. For how and when to check, see AI gets things wrong and using AI safely for your health.

In the other direction, do not dismiss everything either. Some people have heard that AI makes mistakes and decide the whole thing is untrustworthy. That costs you real help. In one study, people rated the very same medical advice as less reliable and were less willing to follow it simply because they were told it came from AI (Reis et al., 2024). For directions, for explaining a confusing term, for drafting what to say, and for getting ready for a visit, AI is genuinely useful. The goal is not to fear it and not to lean on it blindly. The goal is to use it for what it is good at and keep a person in the loop for what matters. The rest of this guide gives you the concrete tools: which facts to double-check, how to check one, what to ask next, and how to say it back.

Which facts are riskiest to trust

Not every fact carries the same risk. General, steady explanations are the safest. If you ask what a copay is, or what the word “referral” means, an AI answer is usually solid, because that kind of information does not change from week to week or from town to town.

The riskiest facts are the ones that are specific, local, and changing. These are the ones to double-check every time:

• Phone numbers and addresses. Small details that are easy to get slightly wrong.
• Costs and copays. These depend on your plan and change often.
• Hours and wait times. A clinic’s schedule this month is not something an AI can know.
• Who qualifies. Eligibility rules are detailed, they are local, and they change.
• Whether one clinic takes your one plan. That is two changing facts at once, so it needs a real check.

A simple rule of thumb: the more a fact is tied to one place, one plan, or right now, the more it deserves a phone call. This is also where over-trusting an AI does the most harm. Researchers call it automation bias, a well-documented tendency to accept an automated tool’s answer without checking it, even when a quick check would have caught the mistake; a systematic review in health care found this pull is real and common (Goddard et al., 2012). Knowing which facts are risky is how you decide where to spend your one phone call.

How to check one fact before you act

Most of the time you are not checking a whole answer. You are checking the one small fact your next step depends on. Here is a routine you can use every time:

Here are three examples from real steps people take to reach mental health care.

Does this clinic take my insurance? The AI says a clinic near you accepts your plan. That is a helpful lead, and it can also be out of date, because clinics change which plans they accept. Treat it as a draft. Then confirm it two ways: check the clinic’s own website, and call the clinic and read them the plan name and the member ID from your insurance card. Ask, “Do you take this plan for a new patient?” The answer you can act on is the one the clinic gives you on the phone.

Is this the right phone number? The AI gives you a phone number for a clinic or a crisis line. A phone number is exactly the kind of small, specific fact an AI can get wrong or mix up. Before you save it, look for the same number on the clinic’s official website. If the two do not match, trust the website. One number you never need to look up: to reach the Suicide and Crisis Lifeline in the US, call or text 988.

Do I qualify for this program? The AI says a low-cost or sliding-scale program is open to you. Eligibility rules are local, detailed, and they change, so this is a draft, not a yes. Ask a follow-up: “What are the exact rules for who qualifies, and what would I need to bring?” Then confirm those rules with the program itself before you get your hopes up or fill out a long form.

Notice the pattern in all three. The AI is good at pointing you in a direction. A person or an official page is what makes the fact safe to act on.

Good follow-up questions to ask

An AI answer gets better when you keep asking. You do not have to accept the first reply. These follow-up questions work for almost any question about getting care:

• “Where does this come from? Is there an official website I can check?”
• “How sure are you about this, and what might make it wrong?”
• “When could this have changed?”
• “What would I need to bring or have ready?”
• “What is a good question to ask the clinic when I call?”
• “Can you say that again in simpler words?”
• “What am I not thinking to ask?”

That last one is quietly powerful. It asks the AI to fill in the gaps in your own plan, which is one of the things it does best.

Say it back in one line

The fastest way to catch a problem is to put the answer into your own words before you act on it. After the AI replies, finish this sentence out loud or in the chat:

“So what you are telling me is…”

Then read your version back to the AI and ask, “Did I get that right?” Two good things happen. If you misread the answer, you catch it now instead of at the clinic door. And if the answer itself was shaky, saying it back in plain words often makes the weak spot show up. This is the same move you would use with a nurse or a caseworker: repeat it back, and confirm before you leave.

Sources

• Buçinca, Z., Malaya, M. B., & Gajos, K. Z. (2021). To trust or to think: Cognitive forcing functions can reduce overreliance on AI in AI-assisted decision-making. Proceedings of the ACM on Human-Computer Interaction, 5(CSCW1), Article 188. https://doi.org/10.1145/3449287.
• Goddard, K., Roudsari, A., & Wyatt, J. C. (2012). Automation bias: A systematic review of frequency, effect mediators, and mitigators. Journal of the American Medical Informatics Association, 19(1), 121–127. https://doi.org/10.1136/amiajnl-2011-000089.
• Kim, S. S. Y., Liao, Q. V., Vorvoreanu, M., Ballard, S., & Vaughan, J. W. (2024). “I’m not sure, but…”: Examining the impact of large language models’ uncertainty expression on user reliance and trust. In Proceedings of the 2024 ACM Conference on Fairness, Accountability, and Transparency (FAccT ‘24). Association for Computing Machinery. https://doi.org/10.1145/3630106.3658941.
• Reis, M., Reis, F., & Kunde, W. (2024). Influence of believed AI involvement on the perception of digital medical advice. Nature Medicine, 30(11), 3098–3100. https://doi.org/10.1038/s41591-024-03180-7.

LINC is a research prototype, not a medical or legal service. Translation can be wrong; you have the right to a free, qualified interpreter. For anything high-stakes, do it with a community health worker or someone you trust. In a crisis, call or text 988, or 911 for immediate danger.