AI hallucinations: why models make things up and how to catch it
In short: a hallucination is a confident, plausible, but invented AI answer. It's not a bug — it's how the technology works: the model predicts a likely text continuation, and when it doesn't know the answer, the "likely continuation" still looks like a real answer. Defence: verify what matters, give the model sources, enable web search.
Why models make things up
A language model doesn't "look up answers in a database" — it generates text token by token, picking the most likely continuation. It has no built-in "I don't know" flag: when facts run out, statistically likely text still gets produced — with dates, names and a confident tone.
Where the risk peaks
- Links and sources — models easily invent plausible URLs and paper titles
- Exact numbers, dates, quotes
- Little-known people and events — little data, much fantasy
- Legal and medical detail — where mistakes cost the most
4 defence techniques
- Verify what matters. Any number, quote or link something depends on — open the original source.
- Permit uncertainty. Add "if unsure — say so" to your prompt. It measurably reduces fabrication.
- Provide material. A model with a document answers from the document, not from memory.
- Enable web search — answers grounded in found pages can be checked by their links.
The one thing to remember
Plausible detail is not proof. A confident tone is not proof. The only proof is the original source you opened yourself.
FAQ
Will hallucinations ever go away?
Models get more accurate, and search plus grounding reduce the risk — but the generative nature of LLMs makes fabrication inherently possible. Verification stays a core skill.