Hallucination

A response generated by an AI model that sounds convincing but is factually incorrect or made up. It’s not a bug: it’s a natural consequence of how LLMs generate text (statistical prediction, not a lookup in a source of truth). It can be reduced but not eliminated entirely, even with RAG or fine-tuning.

When should I use Hallucination at a company?

Understand what it is. Design the system on the assumption that it will happen: human validation for critical decisions, grounding in citable sources, a UI that displays the model’s confidence, and logs to audit errors after the fact.

When is it NOT a good idea Hallucination?

Deploying an LLM directly to end clients or for high-stakes decisions (legal, medical, financial) without verification layers. Every hallucination at the client’s expense damages the brand more than the use case saved.