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AI Hallucination

Allucinazione (AI Hallucination)

Hallucination, in the context of artificial intelligence, refers to the phenomenon where a language model generates plausible but false or invented information, presenting it with the same confidence it would present correct information. The term refers to the model's tendency to "fill in gaps" with data that doesn't exist in reality.

Definition

Common examples of hallucinations include: citing scientific articles with invented titles and authors, referencing non-existent laws or regulations, attributing statements to real people who never made them, presenting wrong math calculations as certain, or describing historical events that never happened.

Hallucinations are a concrete risk in professional AI use, especially in legal, medical, financial and journalistic settings. Mitigating them means always verifying critical information, using techniques like RAG (Retrieval Augmented Generation) to anchor the model to verified sources, and training professionals in critical, informed AI use, a goal Grace pursues through its training scenarios.

Related terms

• LLM (Large Language Model)
• RAG (Retrieval Augmented Generation)
• Fine-tuning
• Inference
• Prompt Injection

More in AI Fundamentals

• LLM (Large Language Model)
• Token
• Context Window
• Temperature
• Fine-tuning
• RAG (Retrieval Augmented Generation)
• Embedding
• API

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