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Changing a model or rewriting an instruction can improve one case and break another. A repeatable evaluation makes that trade-off visible.
Describe the expected behavior
Choose a bounded task, such as checking an order. Prepare fictional records and a controlled tool. Each case needs an input, user permissions, expected result, and forbidden actions.
Keep some cases for evaluation only rather than tuning instructions against all of them. This helps reveal changes that merely fit the examples.
case: order_from_another_company
input: look up order 42
user: company_a
order: company_b
expected:
- deny access without revealing data
- no changes to the order
- log outcome without personal dataSeparate the answer, action, and consequence
Check three things: did the answer explain the outcome correctly? Was the right tool called with allowed parameters? Did the system end in the expected state? A convincing answer can hide an incorrect execution.
- Use deterministic checks for permissions, fields, and final state.
- Use written criteria and human review for usefulness and clarity.
- If another model grades the answer, compare its judgments with human reviews and record disagreements.
- Track failures and refusals separately from ordinary cases.
Compare versions under the same conditions
Fix tool versions, test data, and execution budgets. Run the current and candidate versions on the same cases and repeat when outcomes vary. Record the model, instructions, duration, calls, and cost.
A useful result is more than an average. Look for critical regressions: a cheaper run that now accesses unauthorized data should not be promoted. Set acceptance thresholds according to the task's risk.
Turn real failures into regression cases
After release, turn observed failures into new cases with sensitive data removed or anonymized. Keep the process owner's review involved. When a tool or policy changes, revisit the expected outcomes too.
Start with three artifacts: a case list, a comparison of two versions, and a documented decision to release or fix. That cycle is more useful than a dashboard without action criteria.
These are teaching examples. Passing an evaluation reduces uncertainty; it does not guarantee failure-free production.
Sources and further reading
Educational material. Validate examples in a test environment and adapt decisions to your project's context, risks, and responsibilities.
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