AI & TECHNOLOGY / PRACTICAL GUIDE
AI Hallucinations: Why Confident Answers Can Be Wrong
Understand AI confabulation and use a verification routine before relying on generated claims.
The short answer: An AI hallucination, called confabulation in NIST guidance, is generated content that is false, erroneous or inconsistent while being presented confidently. It arises from how generative systems produce likely outputs, not from human-style belief.
Confidence is a style signal
Grammar, detail and citations can look authoritative even when the underlying claim is wrong. Treat presentation and evidence as separate things.
Verify at the claim level
Break an answer into checkable statements. Open the cited source, confirm it exists, and check whether it supports the exact wording.
Reduce avoidable errors
Provide source material, request quotations with locations, constrain the task and ask the model to mark uncertainty. These steps help but do not guarantee correctness.
Use a stopping rule
If a claim is consequential and cannot be traced to a suitable source or expert, do not act on it. Record the uncertainty instead.
Put it into practice
Try this: Take one generated answer and split it into individual claims. Open every citation and label each claim supported, unsupported or still uncertain.
Sources and further reading
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Educational purpose: This guide provides general education. It does not provide personalised financial, investment, legal or tax advice.