Why AI hallucinations happen and how to manage them

Generative systems produce plausible continuations, not guaranteed facts. Fluent language can therefore make unsupported output look more reliable than it is.

Key takeaways

• Plausible language is not proof of a factual retrieval process.
• Missing, obscure or current information increases unsupported output risk.
• Sources, uncertainty and independent verification must be built into the workflow.

Recognise the conditions

Risk rises when the request needs obscure facts, current information, precise quotations, unavailable private context or an answer despite missing evidence.

Change the instruction and evidence

Provide approved sources, permit the system to say information is missing and request claims, assumptions and citations separately.

Verify outside the model

Open original sources, recalculate important figures and use a qualified reviewer for consequential content.

Do not ask confidence to prove accuracy

A self-reported confidence score can also be generated without reliable grounding. Prefer evidence that a reviewer can inspect and tests against known answers.

Use explicit “insufficient information” and escalation outcomes instead of forcing completion.

Measure failures by consequence

Track unsupported claims, wrong citations, missed uncertainty and correction effort. Give higher weight to errors that could affect rights, money, safety or public trust.

Action checklist

1. Identify every material factual claim.
2. Require approved sources or mark evidence as missing.
3. Open citations and verify context.
4. Recalculate important figures independently.
5. Route consequential output to a qualified reviewer.

A sensible next step

Take one recent AI-assisted document and create a claim ledger showing source, verification status and reviewer. Continue with the practical AI recipes, compare the reviewed AI tools or use the AI Finder to narrow your next decision.