Eliminate uncertainty in shipping AI features
“Does it work?”... “Is it good enough?”... “Can we ship it?”...
How do you answer these questions for AI products? You’re responsible for “running evals” but what does that mean?
How do you choose the right metrics, interpret fuzzy results, and make a confident decision?
This course gives you a framework to do just that.
Map user value to evaluation (eval) objectives so your metrics aren’t abstract. Define success then translate it into measurable criteria.
Choose metrics you can actually maintain: capability, safety, UX friction, latency, cost and “does this reduce support tickets or increase activation.”
Set ship/no-ship thresholds you can defend to leadership.
Build lightweight workflows that work in real teams: human review where it matters, automation where it lasts, documentation that drives decisions.
Consider domain constraints (e.g., healthcare safety) and know what to avoid: silent failures, misleading proxy metrics and tests that don’t reflect production.
Tie everything to ROI: impact vs unit cost, eval coverage vs reliability, and the minimum viable monitoring you need post-launch.
Experience AI evals through a case-based approach with a real AI product that we evaluate together.
What you’ll learn
Acquire and develop a critical skill for product managers who are leading and contributing to AI products.
Learn a repeatable framework for deciding when an AI feature is ready to launch.
Tie decisions to user value, business goals, and measurable evaluation criteria.
Turn fuzzy product goals into concrete eval objectives and measurable success criteria.
Define “good enough” in plain language before choosing metrics or tools.
Use a PM-friendly menu of metrics to avoid misleading proxies and anchor on business value.
Balance capability, latency, UX friction, and cost without being an ML engineer.
Create ship/no-ship thresholds tied to KPIs, risk, and user impact.
Know when to stop tweaking prompts and when launch should be paused.
Learn what to automate, what to review manually, and how to design sustainable processes.
Produce datasets, golden examples, and error taxonomies your team can reuse.
Understand risks in sensitive domains like healthcare and finance.
Avoid silent failures, weak proxies, and tests that don’t reflect production.
Who this course is for
PMs leading AI features, growth, or platform initiatives
PMs who partner with ML teams and want to set evaluation standards
PMs who need to make clear “ship or hold” calls without doing the engineering
What's included
Live sessions
Learn directly from your instructors in a real-time, interactive format.
Lifetime access
Go back to course content and recordings whenever you need to.
Community of peers
Stay accountable and share insights with like-minded professionals.
Certificate of completion
Share your new skills with your employer or on LinkedIn.
Maven Guarantee
This course is backed by the Maven Guarantee. Students are eligible for a full refund up until the halfway point of the course.
Course syllabus
Week 1
Dec
4
AI Evals for PMs - AI Evals and You
Week 2
Dec
9
AI Evals for PMs - Foundations, Scoping, and Datasets
Dec
11
AI Evals for PMs - Metrics, Thresholds, and Ship-readiness
Schedule
Live sessions
3 hrs / week
Thu, Dec 4
5:00 PM—6:30 PM (UTC)
Tue, Dec 9
5:00 PM—6:30 PM (UTC)
Thu, Dec 11
5:00 PM—6:30 PM (UTC)
Projects
2 hrs / week
Async content
1 hr / week
Frequently asked questions
Save 25% until Monday
$2,000
USD