Hallucination in AI refers to the phenomenon where a model generates information that appears plausible but is entirely false or fabricated. This occurs when the AI overgeneralizes patterns from its training data or attempts to respond to prompts with insufficient context or relevant knowledge. In practical applications, hallucinations can lead to the creation of inaccurate facts, nonsensical reasoning, or misleading content, undermining trust and reliability. For example, an AI might confidently provide incorrect details about an event, cite nonexistent sources, or invent technical explanations. Addressing hallucination involves improving training data quality, implementing mechanisms to verify generated outputs, and enhancing the model’s ability to acknowledge uncertainty when it lacks sufficient information.