AI trust question
Hallucinations usually hide inside confident, specific wording. The practical job is not to judge tone, but to verify whether each important claim is actually supported.
You detect AI hallucinations by checking whether the answer makes claims that are unsupported, unverifiable, or inconsistent with your provided context.
The most common pattern is a precise statement with no support: exact numbers, dates, policy rules, or citations that cannot be traced back to a source.
Another red flag is context drift. The model answers a nearby question and sounds useful, but the final output does not match the constraints in your prompt or provided context.
You will also see fabricated authority language, where the answer names a policy, document, or standard that does not exist.
Start by isolating the claims users will act on, then verify those first. This catches high-impact mistakes quickly without over-reviewing low-risk text.
If a claim cannot be verified with the available evidence, route the answer to review instead of treating confidence as proof.
An audit gate gives you a consistent allow, review, or block decision before output reaches users.
That turns hallucination detection into a repeatable release control instead of an ad hoc manual check.
The answer cites an internal policy memo and a fixed compliance deadline, but neither appears in the provided source documents.
Next step: Block this output and regenerate with source-backed claims that map directly to the provided context.
AI hallucination is when a model gives a confident answer that is not reliably supported by facts or evidence.
LLMs optimize for plausible language generation, not guaranteed factual truth in every answer.