Before expanding the use of AI, evaluate answers with real examples

A convincing answer is not necessarily correct. Before putting an AI function into your workflow, build representative examples and systematically compare the output to what the task requires.

Build an evaluation set

Gather common, incomplete, ambiguous, and unusual entries. Remove or protect personal data. For each case, write criteria for success and unacceptable errors. Use examples that represent the variety of the work, not just the easy cases.

Evaluate separate dimensions

Check fidelity to sources, coverage of necessary points, readability and behavior when information is missing. An answer can be well written and omit the essential caveat. Record examples of successes, errors and doubts, and ask for a review from someone who knows the process.

Repeat after changes

Compare versions of the statement, template, or flow with the same set of cases. This helps identify regressions. Initial assessment does not guarantee future performance: inputs, products and models may change. Monitor samples with access controls and a clear path to correct failures.

Don't use a single average to hide a serious error. If a wrong result could cause harm, set limits, human review, and suspension of use before starting the pilot.

Frequently asked questions

How many examples are needed?

Enough to represent the most important and difficult cases. A small sample size is a start of diagnosis, not a statistical guarantee.

Can I let the AI ​​evaluate it itself?

It can help organize comparisons, but it should not be the sole judge of a task with consequences. Use explicit criteria and review by responsible persons.

Does the test need to be redone?

Yes, when instructions, data, model or process change and periodically during use.

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