A hallucination is a claim produced by a model that sounds plausible but is unsupported or false. Examples include an invented citation, a nonexistent API, or a wrong date stated with confidence.
LLMs learn to generate likely text, not to verify every statement. Their training data can be incomplete, outdated, or conflicting. Knowledge stored in model weights is also an imperfect compression of that data. When a prompt requires missing information, the model may continue with a pattern that resembles a good answer.
Applications should assume hallucinations can occur. Useful controls include retrieval from trusted sources, calculation tools, and clear permission to admit uncertainty. Tie citations to source text and evaluate whether claims have factual support.
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