What is a Hallucination in AI?
A hallucination is a model output that sounds confident but is not grounded in facts.
Technical detail
Hallucinations happen when a model fills gaps with plausible but incorrect content. The risk increases when context is thin, prompts are vague, or tasks require exact factual detail. Mitigation includes retrieval, tighter prompts, rule checks, and human review for high-stakes actions. Teams should track hallucination-like errors as an operational metric.
Why it matters
- Incorrect outputs can damage trust and decision quality.
- Customer-facing mistakes can create legal or compliance risk.
- Detection and prevention improve workflow reliability.
- Clear controls reduce escalation burden on teams.
Example
A model drafts an answer about refund policy but cites a rule from an outdated version. Retrieval with current policy docs and an approval check prevents the wrong response from being sent.
How Retailbridge relates
Retailbridge reduces hallucination risk by grounding workflows in approved sources and policy checks. Teams can inspect traces, review exceptions, and improve controls based on weekly quality signals.