AI Product & Experimentation
Define outcomes, run experiments, and decide what to ship.
Define a user outcome and its baseline, design an A/B test with cost guardrails, and decide whether to ship when the evidence conflicts.
3 questions
Questions
Easy
1.5 h
Explain why longer sessions do not justify shipping AI hints, then use a fictional pilot's per-arm figures to propose an evaluation with a real baseline, an independent learning outcome, and guardrails.
Medium
3 h
Write the experiment contract and sample-size plan for a staged-hints A/B test, implement a validated per-arm summary of outcomes and costs, and explain which checks and guardrails must pass before a lift can justify shipping.
Hard
4 h
Check the validity of a synthetic A/B test of an AI coach, compute its rates, intervals, and costs, and defend a release decision when offline ratings, immediate metrics, and matured outcomes disagree.
What you will be able to do
- Rewrite a model capability as a user outcome for a named population and situation
- Choose a baseline that represents the real alternative users would have
- Classify metrics as primary, guardrail, diagnostic, operational, or data quality, and define a numerator, denominator, unit, and window
- Explain what offline evaluation can and cannot tell you compared with a randomized online experiment
- Compute absolute and relative change from per-arm counts and say what they do not establish
- Explain why more engagement, such as longer sessions, is not evidence of benefit on its own
- Write an experiment contract that fixes the population, assignment unit, metrics, tolerances, window, analysis, and decision owner before launch
- Choose an intent-to-treat population and explain when exposure-based analyses are only secondary
- Design assignment, exposure, and outcome events that deduplicate cleanly and join to the authoritative assignment record
Hands-on exercises
Interview rounds that use this topic
Topic boundaries
This topic covers user outcomes, baselines, online experiments, cost guardrails, and ship decisions. Evaluation & Observability covers offline evaluation mechanics.
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Practice problem
Code a validated per-arm summary of success rate, cost, and lift that returns null when a denominator is zero.
System Design
Covers shadow mode, canary, and A/B stages for a model release, with rollback as one registry action.
AI Engineering
The offline half of a ship decision: slices, calibrated graders, and stated uncertainty before users see a change.
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Explains deduplication, late-arriving events, and data quality tests, the plumbing behind trustworthy experiment metrics.
AI Engineering
Applies baselines, a comparison team, and full cost per order to a team's AI pilot instead of a user-facing feature.
Community article
A practitioner's guide to experiment flags bucketed by a stable user ID, kept apart from release flags and kill switches.