What is an AI hallucination?
When a model states something false as if it were true, such as a wrong date, a made-up quote or a source that does not exist.
beginner · Also called Fabrication, Confabulation
Ask a model a question it cannot really answer and it often answers anyway, in the same calm, confident voice it uses for everything else. A wrong date, a quote nobody said, a court case or a book that does not exist. That is a hallucination.
A real example
In May 2025 researchers asked the DeepSeek app, then running DeepSeek-V3, for the birthday of one of the researchers, and told it to answer only if it knew. In 3 tries it gave 3 different dates: 03-07, 15-06 and 01-01. All 3 were wrong. The real date is in autumn.
It did not say "I don't know". It guessed, 3 times, with confidence.
Why it happens
A model is not a database. It does not keep copies of what it read in training. It keeps a huge set of numbers, its weights, that let it predict the next piece of text. Facts that appeared many times in that text come out reliably. A fact that appeared once, like one person's birthday, is close to a guess.
The same research found a second cause: the way models are trained and tested rewards a guess over an honest "I'm not sure". On a test that marks a blank as wrong, guessing scores better on average. So the model learns to guess.
How to catch it
The model's tone tells you nothing. A wrong answer sounds exactly as sure as a right one. What helps:
- Ask for sources, then open them. A model with web search can cite the pages it read. A citation you have not opened is not a check.
- Give it the material. Paste the document or connect it to your files, so it answers from text in front of it rather than from memory. This is the idea behind RAGRAGA way to answer from your own documents. A search finds the passages that match the question, and they are added to the prompt before the model writes..
- Be most careful with rare facts. Names, dates, figures, quotes and references are where guessing hides.
What to do with this
Treat a model's answer as a confident first draft. For anything you will repeat, sign or send, check the specific facts against a source you can see. Making the model check a claim against sources reduces hallucinations. It does not end them.
Sources
Where each fact comes from
This page was last checked against these sources on 14 October 2026.
- 1Why Language Models Hallucinate
Adam Tauman Kalai and others, arXiv · 14 October 2026
The birthday prompt sent to DeepSeek-V3 through the DeepSeek app on 11 May 2025, its 3 answers (03-07, 15-06, 01-01) and that the correct date is in autumn. A base model is expected to get wrong at least the share of such facts that appeared once in its training text. Training and evaluation reward guessing over admitting uncertainty.
- 2How ChatGPT and our foundation models are developed
OpenAI Help Center · 14 October 2026
A model is a large set of numbers called weights or parameters and does not store or retain copies of its training data.
- 3Reduce hallucinations
Anthropic, Claude API documentation · 25 September 2026
Models can state things that are wrong with confidence, and checking a claim against sources reduces this.
- 4Web search tool
Anthropic, Claude API documentation · 14 October 2026
With web search, the service runs the searches and gives the results to the model, which answers beyond its knowledge cutoff and cites its sources.