Hallucination in AI refers to an artificial intelligence system generating information that is false, misleading, or entirely fabricated while presenting it as if it were true.

These errors occur when a model produces outputs that are not supported by its training data, misinterprets patterns, or constructs responses that appear coherent but have no factual grounding.

More formally, hallucinations arise when large language models or vision systems infer nonexistent patterns, objects, or facts—similar to “seeing” or “asserting” things that aren’t real—often due to biases in training data, overgeneralization, or incorrect internal reasoning.

They are not perceptual experiences like human hallucinations; instead, they are confabulations, meaning the model assembles plausible‑sounding but incorrect statements.

Hallucinations are a major reliability challenge in high‑stakes domains such as law, medicine, and scientific analysis, where fabricated details can lead to harmful decisions or misinformation.

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