AI hallucinations occur when large language models generate plausible‑looking but false content. From legal briefs citing nonexistent cases to medical bots misreporting imaginary conditions, these errors span many domains and can have serious consequences. Experts explain that gaps in training data, vague prompts, and the models’ drive to produce confident answers contribute to the problem. While some view hallucinations as a source of creative inspiration, most stakeholders emphasize the need for safeguards, better testing, and clear labeling of AI‑generated output.
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