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Because large language models generate text by predicting the most statistically likely next word rather than retrieving stored facts, they can easily invent plausible-sounding but entirely incorrect information. Hallucinations are a primary risk factor in relying on AI for factual, legal, or medical advice without human review or grounding techniques.
Instances where an AI model generates false, nonsensical, or unverified information, presenting it confidently as fact.
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Models do not "know" things in a traditional database sense. If a model lacks training data on a specific, niche topic, its mathematical probability engine will simply stitch together related concepts that sound correct in context, but are factually wrong.
Currently, no. However, techniques like Retrieval-Augmented Generation (RAG) and strict prompting can drastically reduce their frequency by forcing the model to cite provided sources.
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