A hallucination is an unsupported or false output that may sound plausible. It is not eliminated by more polite prompting; it is controlled through sources, review procedures, task boundaries and accountable approval.
What the concept actually means
LLMs optimise plausibility rather than truth. High-risk patterns include fabricated citations, incorrectly combined facts, plausible numbers and claims beyond the supplied context.
Why it matters in the enterprise
The greater the impact of a claim, the stronger source grounding and review must be. Creative wording needs different controls from contractual, safety or financial information.
A controlled method
The CTPM practice framework for controllable AI applications uses seven stages: understand the task, clarify context and data, apply AI deliberately, review professionally, handle deviations, approve accountably and document transfer. It is a transparent working framework, not a certification.
- Define task and impact
- Clarify data, context and permissions
- Review against domain criteria
- Control deviations, approval and evidence
CTPM practice example
CTPM practice example: In document analysis, every extracted contractual deadline must include document, page and original passage. Without evidence, the value is marked unverified and is not processed further.
Quality and test criteria
The following criteria make quality observable for this use case:
- Claims have verifiable source links.
- Samples include known edge and failure cases.
- Uncertainty is exposed rather than masked by fluent language.
- Errors trigger a defined correction path.
Risks and common misconceptions
A common mistake is asking “Are you sure?”. The model may respond with greater confidence without gaining better evidence.
Example transfer artefact
Transfer artefact: a review matrix mapping claim type and impact to required evidence, reviewer and approval rule.