5 min read
Use business records to check an AI task
Separate learning examples, test cases, and recorded human feedback.
Decide what you need to learn or check
Training uses examples to change a model’s behavior. Evaluation tests the behavior of a system against a stated task and success criteria. Existing human feedback records describe what a reviewer accepted, corrected, or rejected.
These uses can require different permissions, fields, and preparation. State the intended use in the buyer brief before selecting a source.
Write a task with a checkable result
A useful test case states the starting request, relevant context, permitted actions, and the result that counts as success. Record acceptable alternatives rather than treating one historic answer as the only possible answer.
For an illustrative delivery case, success might require an approved route, a feasible date, and a correctly recorded exception. A fluent reply that invents a delivery date should not pass those checks.
Keep the answer out of the input
Separate what the system receives from what the evaluator uses to judge the result. An evaluation that exposes the resolution inside its input can make a task artificially easy.
Also separate records used for training from those used for testing. Near-duplicate tasks or copies of the same source can cross that boundary even when their identifiers differ.
Document how a reviewer decides
Record the criteria, labels, and rules for disputed or incomplete cases. Keep factual correctness, policy compliance, and style preferences separate where the task needs that distinction.
An existing employee correction may offer useful evidence. It is not automatically a reliable reference answer. Review the reason, source context, and whether a second acceptable response was possible.
Record the limits of the result
Keep the tested system version, task set, scoring rules, and any exclusions with the findings. Identify missing outcomes, uncertain labels, and tasks that were not tested.
A pass rate for one dataset does not prove performance for every future task. A later model, prompt, workflow rule, or data update may need a new evaluation.
Put it into an inventory.
Describe the records your team holds. You can start without a raw export.
See if your data qualifies