AI governance and evaluation: shipping responsibly
Putting AI into a product is a commitment to keep it accurate, safe, and accountable. Governance and evaluation are how you keep that commitment.
Define what good looks like
Before building, write down what a correct, safe, and useful answer means for your use case. Turn it into a test set of real examples, including difficult and adversarial ones. If you cannot measure quality, you cannot improve it.
Evaluate continuously
Run your test set whenever a prompt, model, or data source changes, the same way you run automated tests for code. Track results over time so a regression is caught in review, not by a customer.
Protect data and privacy
Decide what data a model may see, where it is processed, and how long it is kept. Remove or mask sensitive fields, apply the same access rules as the source systems, and document the flow so that audits are straightforward.
Plan for failure
Models will sometimes be wrong. Add confidence checks, clear fallbacks to a human, and a simple way for users to report a bad answer. Monitoring in production closes the loop and keeps the system improving.