An AI hallucination occurs when a language model generates information that sounds plausible but is factually incorrect, fabricated, or not grounded in its training data or provided context. Hallucinations happen because LLMs are trained to produce statistically likely text, not verified facts. They can manifest as invented citations, false statistics, non-existent people or events, and confidently wrong answers. Mitigation strategies include RAG (retrieval-augmented generation), grounding with external data sources, chain-of-thought reasoning, and human verification. Reducing hallucination rates is a major research focus across all AI labs.
Frequently Asked Questions
What is AI hallucination?
AI hallucination is when a language model generates plausible-sounding but factually incorrect information, such as invented citations, false statistics, or fabricated events.
How can you reduce AI hallucinations?
Common strategies include RAG (retrieval-augmented generation), fact-checking with external sources, chain-of-thought prompting, lower temperature settings, and human review.