Meaning in graphics work
Model evaluation checks an AI-assisted system against defined tasks, criteria and evidence. It helps understand its behaviour and practical limits under the conditions actually tested.
It differs from trusting a polished demonstration or assuming one successful answer proves general reliability. Fixtures, adversarial cases and real workflow checks answer different questions.
Example and practical consequence
A tender assistant can be evaluated for preserving source clauses, refusing unsupported credentials and keeping rival bid teams separate. Arithmetic and grants should still use verified deterministic mechanisms rather than a model's confident explanation.
Define expected outcomes and keep the evaluated version, inputs and results. Report failures and untested conditions, and repeat relevant checks when the model, retrieval or prompts change. Evaluation supports accountable deployment but cannot create universal safety, legal correctness or production authority merely because an aggregate score appears favourable.
Take it to the next job
- Use representative failure cases and retain the exact evaluated configuration.
