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IJONIS
AI Fundamentals

Hallucination

An AI hallucination occurs when a Large Language Model generates plausible-sounding but factually incorrect information. The model "invents" facts, sources, or connections because it operates on statistical probability rather than knowledge. Hallucinations are one of the biggest risks in enterprise AI deployment.

Why does this matter?

Hallucinations can have serious consequences in business: incorrect legal information, invented product specifications, wrong financial data. For mid-sized companies, it is critical to equip AI systems with validation layers — blindly trusting AI answers is negligent.

How IJONIS uses this

We minimize hallucinations through a multi-layered approach: RAG connection to verified data sources, structured output validation, confidence scoring, and automatic source verification. For critical applications, we integrate human-in-the-loop stages requiring human approval before propagation.

Frequently Asked Questions

Can AI hallucinations be completely prevented?
With generative models, they cannot be completely prevented — but reduced to a minimum. RAG with source references, output validation, and clear prompt instructions reduce the hallucination rate to below 2%. For critical decisions, human review remains essential.
How do I recognize whether an AI response is hallucinated?
Watch for missing or incorrect source citations, surprisingly specific details, and contradictions upon follow-up questions. Technically, we use confidence scores and force the model to cite sources — answers without source evidence are automatically flagged as uncertain.
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