Monitoring an AI Feature After Launch
Know within days, not months, when your AI feature starts getting worse, slower or costlier
A taste of a lesson
We want an LLM to grade every output nightly so we don't need human reviewers. Is that reasonable?
It can be part of the plan, but not a replacement for humans at the start. A model grader is a measuring instrument, and you don't know its error until you test it. Have two people grade around 100 real outputs with your rubric, run the grader on the same items, then compare item by item. Where they disagree, look for patterns, such as the grader rewarding long answers. Keep a small human sample every week after that. Quick check: what would you do if the grader and your reviewers disagreed on a third of the items?
Written by the teacher as an example. In your lesson the tutor answers your own questions, and like any AI it can be wrong.
What you will be able to do
- Choose quality, behaviour, cost, latency and safety metrics suited to your feature's risk
- Set up sampled rubric grading and calibrate any model grader against human labels
- Detect drift in inputs and upstream model behaviour with a frozen evaluation set
- Write an incident playbook with severity levels, owners and a kill switch
- Run a weekly review that turns monitoring data into product decisions
Lesson plan
- 1 Risk decides how hard you monitor Match monitoring depth to who sees the output and what a bad output costs. Start
- 2 Measuring quality on real traffic Build a sampled grading routine with a rubric two people can apply consistently. Start
- 3 Model graders and their calibration Use automated grading only after measuring how well it agrees with humans. Start
- 4 Behaviour, cost and latency signals Read implicit user behaviour, spend and speed alongside quality scores. Start
- 5 Drift and upstream changes Notice when inputs shift or the underlying model changes, and respond deliberately. Start
- 6 Incidents and the weekly review Prepare a playbook for bad days and a routine that turns data into decisions. Start
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About this tutor
For product managers, engineering leads and founders who have shipped an AI feature and need to know whether it keeps working. Launch is when real inputs arrive, and real inputs differ from your test set. You will design a monitoring plan that combines sampled quality grading, user behaviour signals, cost and latency tracking and safety checks, and you will learn how to respond when something shifts, including after a model provider changes behaviour underneath you. Lessons use your own feature where possible. You leave with a metrics list, a weekly review routine and an incident playbook with a clear kill switch.
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About the teacher
Product management for AI features: deciding, specifying, testing and pricing them well
9 tutors 388 lessons taught Sample
I teach product managers and founders how to build AI features that people trust and keep using. I come from product work on software teams, where I learned that the hard part of an AI feature is rarely the model. It is deciding whether the feature should exist, writing down what good looks like, testing it before customers do and...
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