Why AI makes things up, and how to catch it

Ask a model for a fact and it will give you one, confidently, whether or not it is true. The made-up answer looks exactly like the real one: same tone, same certainty, often a citation that turns out to be invented. This is the part of working with AI that catches people off guard, and it helps to understand why it happens before you try to do much about it.

It is guessing, not looking up

A language model does not store facts the way a database does. It predicts the next word from patterns it learned in training. Most of the time those patterns line up with reality, because the truth showed up often enough in the data. When they do not, the model fills the gap with something plausible instead of saying it does not know.

That gap-filling is what people call a hallucination. The model is not lying, because it has no sense of which parts are solid and which it just invented. To the model, both feel the same.

Where it goes wrong most

The risk is highest exactly where you most want a correct answer: specific names, dates, numbers, quotes, citations, and anything after the model's training cutoff. Ask a model for a study to back a claim and it will often produce a title, authors, and a year that look real and do not exist. Recent events are the other weak spot, since a model trained last year cannot know what happened last week.

Two checks that actually help

The first is to give the model real information to work from instead of trusting its memory. Web search grounding does that: the model searches for current sources and answers from what it finds, so the facts come from the page rather than from training. On LLMWeave that is web search in a weave, and it is the right tool for anything time-sensitive or factual.

The second is to ask more than one model. A single model has no way to flag its own blind spots. Run the same question through Claude, GPT-5, and Gemini and the disagreements jump out. Where they agree, you can trust the answer more, because independent models converging on the same invented fact is rare. Where they split, you have found the part worth checking. That cross-check is most of why running several models beats betting on one.

What it does not fix

Neither check makes a model infallible. Grounding is only as good as the sources, and a model can still misread a page it was handed. Agreement can be wrong when every model shares the same gap. The honest framing is that these lower the odds of a confident mistake getting through, not that they remove them. For anything that really matters, read the source and keep a person in the loop.

Hallucination comes from how these models work, so it is not going to be patched away. What you can change is your habit. Ground the answer in real sources when the facts matter, and ask several models when you want a cross-check. Do that and a confident mistake has a much harder time slipping past.

More from the blog