AI Hallucination

Language models predict the next most likely word based on patterns learned during training. They do not look up facts or verify claims. When the model encounters a question it does not have strong training signal for, it fills in the gap with something that sounds right. This is hallucination.

Hallucinations can be subtle. The model might cite a real-sounding but nonexistent research paper, give a plausible but wrong statistic, or accurately describe a policy that does not actually exist. The confidence of the language makes hallucinations hard to catch without independent verification.

For enterprises, hallucination is a deal-breaker in high-stakes applications. A customer support agent that invents a refund policy, a legal assistant that cites a fake precedent, or a medical system that fabricates a drug interaction can all cause real harm.

The primary mitigation is RAG (Retrieval Augmented Generation). By giving the model real source documents to work from, you reduce the need for it to rely on its training data. Combining RAG with instructions like "only answer based on the provided context" and "say when you do not have enough information" further reduces hallucination rates.

Other techniques include grounding checks (verifying outputs against source documents), confidence scoring (flagging low-confidence answers for human review), and using multiple model calls to cross-check answers. No technique eliminates hallucination entirely, but layering them together gets the error rate low enough for production use.