Hallucination
What is Hallucination?
When a model states something false and presents it as fact, with no signal that it's unsure. You reduce it by grounding the model in real data (for example RAG), writing clearer prompts and keeping a human in the loop. It never disappears completely on its own, so it's a risk you manage, not a bug you fix.
Why it matters
A model can state something false and present it with the same confidence as a fact, with no signal that it is unsure. That is the single biggest reason you cannot wire a language model straight into a process that has to be right. It is not lying, and it is not a bug you can patch out. It is a property of how these systems generate text, so the job is to manage it rather than wait for it to disappear. For regulated work, published content, or anything a customer relies on, an unmanaged hallucination is a real liability.
In practice
You reduce it, you do not eliminate it. Grounding the model in real data (retrieval from your own documents) keeps it closer to the truth. Clearer instructions and a lower tolerance for guessing help. For anything that carries consequence, a person checks the output before it ships. Teams that design around hallucination, rather than getting caught out by it, end up with far more reliable systems.
