How does a problem after launch become a test?

Choose a trace you are allowed to use and determine what actually went wrong. Confident AI can map a matching trace, step or conversation into a dataset. Check which fields are copied and write the expected result independently before the next run.

What might a production trace contain?

What you instrument can include a user input, app output and context retrieved for the answer. Decide what your team really needs to diagnose the failure. Start with approved example data, check stored fields and permissions, and limit what you retain.

What does trace volume have to do with cost?

The pricing calculator uses trace data size and the time you keep it. Estimate how much you log, then confirm retention and evaluation limits for the plan. Keep an approved copy of the cases and scoring rules. The platform documents pulling datasets through its API.

How would you turn one bad answer into a clean case?

Draw a fictional request whose reply tells a customer the wrong cancellation period. Use made-up data to plan what belongs in the dataset.

Keep only what explains the failure
Record the fake question and the approved policy. Leave out personal data the test does not need.
Separate the past answer from the right one
Store what the app actually said apart from the expected answer. Write what a later version must do to pass.