A team reruns a promising test and obtains different results. Without a record of the system and dataset, it cannot tell whether the change reflects normal variation, a revised component or a mistake.
Preserve the evaluation configuration
Record the input version, model identifier, parameters, retrieval settings, tool permissions and evaluation method. Retain the actual outputs used in scoring where appropriate.
PyTorch’s reproducibility guidance notes limits across releases and platforms even with controlled seeds. That is a reminder to state the scope of reproducibility rather than promise identical behaviour everywhere.
For an illustrative multilingual assistant, changing the retrieval corpus can alter answers even when the model and prompt remain unchanged.
Separate repeated runs from changed tests
Repeat under the same recorded conditions to examine variability. If a component changes, label the run accordingly rather than add it to the earlier result as though nothing changed.
Keep evaluator instructions stable. Different reviewers or an updated scoring model can alter the apparent result without a change in the application itself.
Where a provider does not expose every version detail, record the limitation and the information available. Do not invent a level of control the buyer does not possess.
Use the record to support a decision
Explain the variation relevant to the workflow. A system may be acceptable for drafting but unsuitable for an action that requires consistent structured output without additional validation.
Keep the evaluation output, scoring version and reviewer decisions together. A later rerun should be able to distinguish model variation from a changed grading process. This is especially important when another AI system assists evaluation, because a revised evaluator can alter the reported result without improving the product being tested.
GAT’s enterprise pilot guide depends on interpretable test evidence. Reproducibility records make that evidence easier to challenge, compare and update when the application evolves.
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