Hallucination

In artificial intelligence, a hallucination refers to an output generated by an AI model that appears confident and factual but is actually false or unsupported by real data. This phenomenon often occurs in large language models and generative systems when they fill gaps in knowledge or misinterpret training information. Hallucinations can take the form of incorrect facts, fabricated sources, or unrealistic images, depending on the AI application. They highlight one of the biggest challenges in AI development—ensuring reliability, accuracy, and verifiable outputs. Researchers and developers use techniques like model fine-tuning, retrieval-augmented generation, and human oversight to reduce hallucinations and build more trustworthy systems.