Hallucination
Also called confabulation, ai hallucination, making things up
When an AI model states something false or made up as if it were true.
A hallucination is output from an AI model that sounds confident and plausible but is wrong: an invented statistic, a fake citation, a function that does not exist in a library, a quote nobody said. The term is borrowed loosely from psychology; some researchers prefer "confabulation", because the model fills gaps with fluent guesses rather than seeing things.
It happens because language models generate likely-sounding text, not verified facts. When the training data contained the answer, likely and true usually overlap. When the question is obscure, recent, very specific, or based on a false premise, the most likely-sounding continuation may be false. Models also tend to answer rather than admit uncertainty, partly because of how they are trained.
You can reduce hallucinations but not remove them. Giving the model source documents and asking it to answer only from them (grounding), connecting it to search or retrieval, asking for quotes and citations, and explicitly allowing "I do not know" all help. Newer models hallucinate less on common facts but still fail on niche details.
Example
Asked for sources on a narrow topic, a model lists "Smith, J. (2021). Remote Teams and Trust. Journal of Organisational Studies, 14(2)". The journal exists but the article does not. It looked right because the format, author name and title were all plausible.
Common mistake
Asking the model whether its own answer is correct and trusting a "yes". A model can repeat a hallucination with the same confidence. Check important facts against a primary source, especially numbers, names, dates, quotes, citations and legal or medical details.