Make your AI experiments teach you something.

A compelling demo can be the beginning of a useful idea. To learn whether it actually works, give the experiment a question it can answer.

Keep a baseline

Before adding a model to a workflow, record how the task works today. A simple rules-based solution may be enough, and it provides a useful comparison when it is not.

Collect representative examples

A small set of realistic inputs is more useful than a single carefully chosen prompt. Include ordinary cases, ambiguous cases, and cases where the right behavior is to ask for more information.

• Define what a good answer must contain.
• Record examples of unacceptable outputs.
• Keep evaluation examples separate from prompt development examples.

Inspect the failures

An average score is only part of the story. Group failures by cause, then choose one improvement to test. Changing the prompt, retrieval, and model together makes it harder to understand which change helped.

The goal is repeatable learning: a record of what you tried, what changed, and what you still do not know.