A phenomenon where an AI model generates incorrect, nonsensical, or unreal information but presents it confidently as fact.
LLMs are probabilistic, not deterministic. They predict the next likely word, not the truth. Sometimes, the most 'likely' sounding sentence is factually wrong.
Hallucinations are the biggest barrier to enterprise adoption. Mitigation strategies include using RAG (grounding), lowering the 'temperature' setting, and human-in-the-loop review.
Hallucination is a feature, not a bug. It's the same mechanism that allows AI to be 'creative' and write poetry. The problem is when we want facts, not fiction.
Understanding this helps managing risk. You never trust a vanilla LLM with a factual query (like 'What is my bank balance?') without a grounding source.
We can fix hallucinations completely.
Reality:Currently impossible with LLM architecture. We can reduce them to near-zero (99.9%), but never 100%. That's why 'Human in the loop' matters.
Smarter models don't hallucinate.
Reality:They actually can be profound/convincing liars. They are better at sounding plausible, which makes the hallucinations harder to spot.
Verification Systems: A second AI model designed solely to fact-check the output of the first model.
Creative Writing: Actually leveraging hallucination to generate surreal plot ideas or unique art concepts.
Legal Review: The infamous case of a lawyer using ChatGPT which cited fake cases—a lesson in why verification is key.
It doesn't 'know' truth. It only knows 'what word usually comes next'. If it doesn't have the data, it guesses the most probable sounding completion.
RAG (providing the facts), Context (telling it to say 'I don't know'), and Temperament (lowering creativity settings).
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