What the term really means
False or unsupported information generated by a model in a convincing form.
How it works in practice
A hallucination may be an invented order number, a nonexistent quotation or a confident condition absent from the source.
The decision to make before implementation
Making the wording sound less certain does not fix missing evidence; the correct response may be refusal or a clarifying question.
How to verify that it works
Measure unsupported claims, error severity, user detectability and escalation effectiveness in critical areas. Compare results with an agreed baseline and review routine cases, difficult exceptions and human hand-offs separately. A practical AI Hallucination test should have an owner, a review date and a recorded example of an outcome the team will not accept.
The PAR HOUSE Agency approach
We approach AI Hallucination from the workflow rather than a tool demonstration. Making the wording sound less certain does not fix missing evidence; the correct response may be refusal or a clarifying question. We then build a small measurable scope, record assumptions and expand only after quality review.
