An LLM processes language as numeric token sequences and predicts likely continuations from its context. It can apply patterns remarkably well, but has neither human understanding nor an inherent truth check.
What the concept actually means
Tokenisation, training, parameters and inference explain the mechanism better than the metaphor of a digital brain. The model reconstructs each answer, so identical questions may yield different results.
Why it matters in the enterprise
For enterprises this implies a clear separation: the model supplies drafts and transformations; dependable organisational knowledge comes from approved sources, rules, databases and human expertise.
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: A project lead uses an LLM to extract actions from minutes. Names, dates and commitments are not accepted blindly; they are checked against the approved record and responsible owner.
Quality and test criteria
The following criteria make quality observable for this use case:
- Model and version are documented.
- Relevant context is complete and permitted.
- Outputs are tested against domain criteria.
- Non-deterministic variation is anticipated.
Risks and common misconceptions
Risks include anthropomorphism, trusting a model’s self-assessment, and assuming that training provides access to current or internal facts.
Example transfer artefact
Transfer artefact: an accessible model card for the intended use, covering purpose, limits, data, review duties and approved tasks.