What is an AI hallucination?
An AI hallucination is an answer that sounds confident and reads well but is factually wrong or entirely invented by the model.
The simple explanation.
A hallucination is a confident, well written, wrong answer. The model invents a policy detail, a product feature or a date, and presents it in exactly the same tone as everything it got right. This is not the software malfunctioning. A language model is built to produce plausible text, and when it does not have the fact, plausible text is still what it produces. That is why hallucinations are easy to miss: nothing about the reply looks unusual.
How to reduce it.
Three measures cover most of the risk. Ground the agent in your real documents and records, so answers come from retrieved material rather than memory. Give it tools to look things up, so stock levels and order status come from the system rather than a guess. And instruct it explicitly to say it does not know and hand over to a person, because a model told only to be helpful will always prefer an answer to an admission. Then read the logs regularly.
What it means for how you deploy.
Hallucination risk is why the first version of a customer facing agent should have a narrow scope and a human check on anything consequential. Start with questions whose answers live in a document you control, keep pricing and commitments behind an approval, and read a sample of real conversations every week. The rate is low with a well built system, but it is never zero, and the businesses that get burned are the ones that assumed it was.
The Voltade take
We would rather an agent say it needs to check with a colleague than invent an answer, and we build for that: retrieval from your own documents, tool calls for anything factual, and an explicit escalation path when confidence is low. We then review real transcripts with you in the first weeks, so gaps surface in a review rather than in front of a customer.
Related pages
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