How I keep AI from making things up

If you've used an AI assistant for more than an hour, you've seen it happen: it tells you something with total confidence, and it's simply wrong. A made-up statistic, a citation that doesn't exist, a fact about your own account that never happened. People call this a "hallucination," which makes it sound exotic. It isn't. It's the most normal thing in the world for these systems, and once you understand why, it's a lot easier to deal with.

What a hallucination actually is

Here's the plain version. A language model isn't a database that looks things up. It's a very sophisticated pattern-completer. It predicts what words should come next based on everything it has seen. Most of the time, the most plausible-sounding next words are also true. But not always. When the model doesn't actually know something, it doesn't stop and say so. It produces the most plausible answer anyway, in the same confident tone it uses when it's right.

So a hallucination isn't the model lying. Lying requires knowing the truth and choosing to hide it. The model doesn't know it's wrong. It's doing exactly what it was built to do: produce fluent, plausible text. And fluency and truth are not the same thing. That gap is the whole problem.

Why this matters more in the tools I build

For a casual chat, a wrong answer is annoying. In the internal tools I've built, things that help people make real business decisions, a confident wrong answer is a serious problem. If a tool tells someone the wrong number about a real customer, or invents a reason a deal is at risk, and they act on it, the damage is real. And there's a second, quieter cost: the moment a person catches the tool being confidently wrong, they stop trusting it. And a tool you have to double-check is slower than not using the tool at all. You've added work while promising to remove it.

So for me, reducing hallucinations was never an academic exercise. It was the difference between something people relied on and something they quietly abandoned.

What actually helps (in plain terms)

You can't make a language model perfect. But you can build the system around it so that it makes things up far less often, and so that when it does, a human catches it cheaply. Here's what I've found actually works.

Make it answer from real data, not from memory. The biggest single fix is to stop letting the model answer from its own head and instead force it to answer from a specific, trusted source you hand it: the company's own documents, the actual records. If the answer isn't in the source, it shouldn't answer. This is the idea behind what people call "grounding," and it's less glamorous than it sounds: mostly it's careful work making sure the right, current information is the only thing the model is allowed to draw on.

Put it on rails. Rather than letting the model do anything it wants, I narrow what it's allowed to do. In one tool, instead of letting it write freeform queries against raw data, I made it work only through a vetted set of definitions, so it literally couldn't invent its own version of a number. Fewer degrees of freedom means fewer places to go wrong. You trade some flexibility for a lot more trust, and that's almost always the right trade.

Make it show its work. Every tool I've built shows the source behind its answer, the document it pulled from, the query it ran, the assumptions it made. This does two things. It lets a person sanity-check the answer before acting on it. And, honestly, it keeps the system honest: an answer that has to point at its evidence has a much harder time being made up.

Let it say "I don't know." This sounds obvious and is surprisingly hard. Left alone, these systems would rather give you a confident guess than admit uncertainty. So you have to actively design for the opposite: make "I'm not sure, can you clarify?" a valid, encouraged answer, and reward it over a smooth guess. A tool that occasionally says "I don't have enough to answer that" is one you can actually trust the rest of the time, because you've learned it won't bluff.

Test for it on purpose. Finally, I write checks that deliberately try to catch the system making things up: feeding it questions where I know the right answer, and questions where the honest answer is "that's not in the data," and confirming it handles both. You can't fix what you don't measure, and hallucinations are no exception.

The mindset that matters

If I had to compress all of it into one idea, it's this: stop trying to build a model that never makes a mistake, and start building a system that's honest about what it knows and easy to double-check. Hallucinations don't go to zero. But you can push them way down. And, just as importantly, you can design things so that when one slips through, a person spots it in a second and it costs almost nothing.

That's the real trick. Not a smarter oracle that's always right. A tool that shows its work, stays inside its lane, and tells you when it isn't sure. That's what earns trust, and trust, not raw intelligence, is what makes people actually use the thing.