You have probably seen it. You ask an AI a question, it gives you a confident answer, and the answer is completely, perfectly, hilariously wrong. A made-up court case. A book that does not exist. A quote from a famous scientist who never said it.
That is a hallucination. The word is misleading — the model is not having a vision. It is doing exactly what it was trained to do. The problem is that what it was trained to do is generate plausible-sounding text, not be correct.
Why it happens
Remember from part 1 — the model is autocomplete. When you ask it "what was the verdict in Smith v. Jones, 2008?", it does not have a database of court cases. It has a sense of what court verdicts tend to sound like. So it generates a court verdict that sounds right. Fluent, confident, formatted correctly. It is also entirely fictional.
The model has no built-in concept of "I do not know this." That is not a value it has access to.
A model that has been trained to always give an answer will always give an answer.
What helps (a little)
- Retrieval. Give the model real documents to look at, and tell it to quote from those. This is what tools like search-augmented chatbots do.
- Calibration. Some newer models are trained to express uncertainty. They are still not great at it.
- Your skepticism. Treat every specific factual claim — names, dates, numbers, quotes — as a hypothesis to verify, not a fact to trust.
What does not help
- Asking the model "are you sure?" It will say yes. Then it will say no if you push. It is autocomplete.
- Trusting it more because the answer was confidently formatted. Confident formatting is the easy part.
The bottom line
Hallucinations are not a bug. They are the default behavior of a system that was trained to always sound right, with no separate training to know when it does not know. Plan your usage around this. The model is a draft generator, not a fact source.
Next, the final part: how to actually use AI day-to-day, without the wheels coming off.