Where the workflow shifted
fm CLI, Python SDK, prompt evaluation, and automation suggest that model behavior needs repeatable checks, not only design intuition.
Teams maintaining AI features need representative inputs, expected outputs, failure examples, and performance metrics.
Tool names are not outcomes
The signal matters when it clarifies search intent, proof, and conversion action, not when it adds another traffic tactic.
Check permissions and failure
- Create 20 representative user inputs and run them before and after prompt or tool-call changes
- Keep the test narrow: one priority page with clear topic, source links, internal links, and a conversion action
What still needs proof
Without an evaluation set, model quality becomes hard to explain and hard to debug. Keep the original source open so the announcement, the evidence, and this site's interpretation stay separate.