An application can look the same while its model or surrounding instructions change. A prompt that produced a useful result earlier may respond differently after an update.
Keep a small set of representative examples for an important recurring task. Include an ordinary case and one that tests a meaningful boundary. Review the outputs against the same expectations rather than judging each new result only by its style.
Read relevant change information where it is available and preserve the context of the comparison. The check does not need to become a large evaluation project. Its purpose is to confirm that the familiar task still works well enough for the way you use it.
Try the idea in context.
Imagine an extraction workflow returning a different treatment of missing fields. A small regression check can reveal whether the new behavior still fits the task.
A thought for the next read.
A small trial should have a clear stopping point. Decide which uncertainty the tool can help explore and what observation would answer the next question.
- Keep representative task examples.
- Include a meaningful boundary case.
- Compare against consistent expectations after a change.
Follow a related question
Define the shared starting condition.
Compare groups that share a startExport a small representative sample.
Choose a tool with an exitKeep learning
Related background to continue exploring this subject.
Google: prompt design strategies Google: an introduction to language models
