Hallucination in artificial intelligence refers to the phenomenon where an AI model generates outputs that are factually incorrect, fabricated, or nonsensical while presenting them with apparent confidence. This is most commonly observed in large language models (LLMs) that produce plausible-sounding but entirely false information, such as inventing citations, attributing quotes to the wrong people, or stating inaccurate statistics as though they were verified facts.
AI hallucinations occur because language models are fundamentally pattern-matching systems trained to predict the most likely next tokens in a sequence rather than to verify factual accuracy. When the model encounters a prompt outside its training distribution or when multiple plausible completions exist, it may generate fluent text that has no basis in reality. The stochastic nature of text generation, combined with the model's inability to distinguish between what it "knows" and what it is fabricating, makes hallucination an inherent challenge.
Mitigating hallucinations is a major focus of AI research and engineering. Common strategies include retrieval-augmented generation (RAG) to ground responses in verified data, fine-tuning with human feedback (RLHF), implementing fact-checking layers, and designing systems that express uncertainty or cite sources. For anyone deploying AI in production, understanding and managing hallucination risk matters for maintaining user trust and avoiding costly errors, especially in high-stakes applications.