AI fundamental

Model behaviour and variability

Tokens, tokenisation, attention, sampling, parameters, repeated runs, and observable variability.

Explanatory foundation

Guiding question

Which generation condition changed, and is the observed difference stable enough to interpret?

Use the answer to interpret evidence and limits rather than to substitute technical vocabulary for observation.

Related units

Where this fundamental is taught

Open the complete Friday guide for the conceptual input, demonstration, experiment, caveat, and transfer activity.

Unit 03

Why Models Behave Differently: Parameters, Variability, and Control

Participants learn to stop treating model variability as inexplicable noise. They learn to hold a task stable, vary one generation condition, repeat trials, and use the resulting evidence to decide when variability is useful, when it is risky, and which controls are appropriate.

Model behaviour and variabilityMechanism literacy