What an AI hallucination is
An AI hallucination is output that sounds right but isn't: an invented statistic, a made-up policy, a wrong product name, a citation to a source that does not exist. The danger is the tone. Hallucinations are delivered with the same confidence as correct answers, so they slip past readers who assume the AI is reliable.
They happen because language models predict likely text rather than retrieve verified facts. When the model lacks the right information, it can still generate fluent, confident-sounding content to fill the gap.
How to catch hallucinations in automations
You reduce the risk at the input side (grounding the model with real data, retrieval, and tight prompts) and catch what slips through at the output side. Output checks include requiring that answers cite provided sources, forbidding specific claims, and using an LLM judge to flag statements not supported by the input.
For high-stakes outputs, a human review step is the backstop: a person confirms anything the automated checks are unsure about before it goes out.
Put AI hallucination into practice with Tracira
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