Plain-language explainer
Why AI hallucinates
Why do AI models confidently make things up?
Because they are built to continue text plausibly, not to report facts. A language model always produces the most likely next words. When the truth is in its training data, likely and true usually coincide. When it is not, the model fills the gap with something that sounds right, in perfect fluent prose, because nothing inside it distinguishes a remembered fact from a plausible pattern. Hallucination is not a glitch on top of the system. It is the system, running without grounding.
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What people get wrong
- Hallucination is a bug that will be patched out. It is inherent to next-word prediction. It can be reduced with grounding and citations, not deleted.
- The model knows when it is making things up. There is no internal fact-checker. Fluency and confidence are properties of the text, not evidence about truth.
- Wrong answers mean bad training data. Even perfect data cannot cover everything, and the model fills every gap by design.
Where you see it in real products
- Lawyers have been sanctioned for filing briefs with invented case citations from a chatbot.
- An airline's support bot invented a refund policy, and a tribunal held the company to it.
- The 'check important info' notice under chat boxes exists precisely because of this.
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