In artificial intelligence, hallucination refers to instances where AI models, particularly large language models (LLMs), generate content that appears plausible but is factually incorrect or nonsensical. This phenomenon occurs when the AI produces information not grounded in its training data or real-world facts, leading to outputs that may mislead users. For example, an AI might confidently provide an incorrect historical date or fabricate details about a non-existent scientific study. Addressing AI hallucination is crucial for ensuring the reliability and trustworthiness of AI-generated content.