Hallucination
Hallucination is when an AI model states something that sounds plausible but is wrong or made up, often because it has no ground truth to rely on.
In Simple Terms
Think of it as a very confident person who sometimes fills in gaps with plausible-sounding guesses.
Detailed Explanation
Models generate text by predicting what comes next; they do not know facts. So they can invent names, dates, or citations. RAG, grounding, and careful prompting reduce but do not eliminate hallucination. When it matters: in legal, medical, or financial contexts where errors have real consequences. Common mistakes: trusting long outputs without verification, or assuming newer models never hallucinate.
Related Terms
Artificial Intelligence
The simulation of human intelligence processes by machines, especially computer systems.
Read moreMachine Learning
A subset of AI that enables systems to learn and improve from experience without being explicitly programmed.
Read moreTransfer Learning
Transfer learning is the practice of taking a model already trained on one task or dataset and reusing or adapting it for another. It speeds development and often improves performance when data for the new task is limited.
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