AI Model Evaluation Checklist — Free Worksheet

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01 / Identify the versionRecord the exact checkpoint and revision. Separate a base model from an instruction-tuned release or community adaptation.

02 / Confirm current accessCheck today’s model card and download or API conditions. An old launch announcement may describe access that has changed.

03 / Read the actual licenceRecord the licence for this checkpoint and the terms for your intended deployment, including any hosting service.

04 / Define the taskWrite down the expected output, intended users and unacceptable mistakes before testing.

05 / Prepare representative examplesUse public, synthetic or appropriately authorised examples, including local names, spelling variation and mixed-language input.

06 / Set a scoring rubricDecide how you will judge correctness, completeness and language quality before seeing the answers.

07 / Test uncertainty and boundariesInclude questions the model cannot answer from the supplied information and requests outside the intended scope.

08 / Keep failure casesRecord factual errors, misleading answers and harmful actions relevant to your use. A fluent answer can still be wrong.

09 / Measure operating requirementsCheck latency, hosting requirements and cost in your own environment. Keep prompts and settings alongside results.

10 / Check deployment and data handlingIdentify the actual hosting provider, processing locations, retention arrangements and access controls.

11 / Plan human reviewDecide which outputs need approval, how mistakes are reported and how the system can be stopped or rolled back.

12 / Record the next decisionChoose a next step and list unresolved questions. Ticking every box does not certify a model as safe or suitable.

Choose with evidence.
Keep your reasoning.

From promising to practical.

A checklist is a starting point.

Choose with evidence.Keep your reasoning.

From promising to practical.

A checklist is a starting point.

Choose with evidence.
Keep your reasoning.