Make Safer AI Decisions
Jai Bhagat
AI vs Human Decision Making Systems
Nicole Mercede
Live Case Study
Make AI product decisions with less risk
You do not need to learn every model, framework, or engineering detail to make better AI decisions. You need data literacy: what to observe, what signals matter, and what patterns tell you whether an AI product is safe, useful, expensive, brittle, or worth scaling.
In this four-week flipped classroom, Jai walks through live examples and real AI data so you can practice reading what is happening inside LLM apps, agents, and model workflows. You will leave with a repeatable way to ask better questions, pressure-test claims, and make safer product decisions at work.
What you’ll learn
By the end, you will have a simple decision system for reading AI data and reducing product risk.
Read traces, evals, logs, and observability data well enough to understand what an AI system is actually doing.
Separate product risk from engineering noise: quality, latency, cost, reliability, safety, and user impact.
Ask engineers sharper questions and make clearer go/no-go, scope, and prioritization decisions.
Learn directly from Jai & Nicole
Jai Bhagat
AI product systems instructor
Nicole Mercede
Family Office Operator turned into Internet Business Owner
Who this course is for
Product leaders, founders, operators & technical teammates who are being asked to make AI product decisions before they feel aligned.
People who need enough technical understanding to be dangerous in the right way: you need to know what data to ask for and how to read it.
Teams facing unclear AI demands who want a practical way to reduce risk, choose where to focus, and explain tradeoffs at work.
Prerequisites
No engineering background required
You should be comfortable using AI tools and discussing product, customer, or business problems. Code examples may appear, but the class is about reading data and making decisions.
What's included
Live sessions
Learn directly from Jai Bhagat & Nicole Mercede in a real-time, interactive format.
Live case discussions
Work through examples of LLM apps, agents, evals, observability, cost, latency, and failure modes.
Peer review
Use the room to compare how different roles read the same data and decide what to do next.
Decision checklists
Leave with reusable questions and patterns for weekly, monthly, and quarterly AI product decisions.
Maven Guarantee
Your purchase is backed by the Maven Guarantee.
Course syllabus
3 live sessions • 13 lessons • 3 projects
Week 1
Week 1: Read the Signals
Nov
20
Session 1
Week 2
Week 2: Diagnose Risk and Tradeoffs
Practice: Compare AI System Behavior
Nov
27
Session 2
Schedule
Live sessions
1-2 hrs / week
Three live case discussions with short async prep between sessions.
Fri, Nov 20
5:00 PM—6:00 PM (UTC)
Fri, Nov 27
5:00 PM—6:00 PM (UTC)
Fri, Dec 4
5:00 PM—6:00 PM (UTC)
Weekly decision drills
1-2 hrs / week
Short exercises built around real AI product choices, risk signals, and tradeoffs.
Async case materials
1-2 hrs / week
Watch or read before class, then use live time for examples, discussion, and peer review.
Frequently asked questions
Maven for Teams
Reimbursement
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Everything L&D needs: email template, receipts, and certificate of completion.
Get reimbursedTeam discount
Learn with your teammates
Save 20%+ when 2 or more teammates enroll in the same cohort.
Save 20%+ with a teamPrivate cohort
Run a cohort for your org
A dedicated cohort with a custom schedule and curriculum, tailored to your team.
Book a private cohort$801
USD