How would you ensure the continuous improvement and evolution of an AI product post-launch?

Example Answer

I would maintain one operating review that joins customer feedback, task outcomes and service health. Record complaints and representative examples with appropriate data minimization, and compare them with the denominator of eligible tasks. Check who is missing from voluntary feedback.

Monitor model errors, completion and escalation rates, latency, cost and relevant user outcomes by segment. DAU is a count of distinct users performing a defined action in a defined day; it is not an engagement rate or proof that the model is useful. Investigate instrumentation and workflow changes before assuming the model needs retraining.

Prioritize improvements with accountable owners. A fix may involve data, retrieval, interface controls or operational process as well as the model. Version candidate changes, run regression and independent evaluation, and require approval for the intended release scope. CI/CD makes delivery repeatable; it does not make every artifact deployable or safe.

Use qualified controlled rollouts with explicit guardrails, observation windows and rollback/limitation criteria. Examine uncertainty and regressions before expanding, then verify that the original problem improved. Retire low-value or harmful features rather than treating continuous change as the goal.