AI Product Hub

August 2025

February 2026 (please delete)

Feb 2026

December 2025

Cohort 4

Cohort 1

Cohort 5

Acquire and develop a critical skill for product managers who are leading and contributing to AI products.

GenAI Product Builder @ Google, ex-Meta

Marily Nika, Ph.D AI/ML

Product Manager AI&ML @ Google

Diego Granados

Senior Data, ML and GenAI Scientist

George Zoto

Senior NLP/AI Scientist

AI Evals for PMs Certification

Director Mainstream Innovation Strategy & Business Development 

Head of Product

Senior Product Manager

Senior Product manager

“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.<ul><li>Map user value to evaluation (eval) objectives so your metrics aren’t abstract. Define success then translate it into measurable criteria.</li><li>Choose metrics you can actually maintain: capability, safety, UX friction, latency, cost and “does this reduce support tickets or increase activation.”</li><li>Set ship/no-ship thresholds you can defend to leadership.</li><li>Build lightweight workflows that work in real teams: human review where it matters, automation where it lasts, documentation that drives decisions.</li><li>Consider domain constraints (e.g., healthcare safety) and know what to avoid: silent failures, misleading proxy metrics and tests that don’t reflect production.</li><li>Tie everything to ROI: impact vs unit cost, eval coverage vs reliability, and the minimum viable monitoring you need post-launch.</li></ul>Experience AI evals through a case-based approach with a real AI product that we evaluate together.

Eliminate uncertainty in shipping AI features

Make confident ship or hold decisions for AI features

Translate user value into clear evaluation goals

Choose the right metrics for capability, safety, UX, and cost

Set defensible thresholds leadership will trust

Build lightweight evaluation workflows teams can maintain

Navigate domain constraints and avoid common failures

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

Go from using Claude Code as autocomplete to running it like a full engineering org — planning, reviewing, QA-ing, and shipping in one flow

CEO @ MindStudio.ai | ex-Product @Google

Google

LinkedIn

Microsoft

Cisco

Dmitry Shapiro

CEO @ Mindstudio, ex-CTO @ MySpace

Most people use Claude Code like a fancy autocomplete. Garry Tan — CEO of Y Combinator — uses it like a full engineering org: CEO, eng manager, staff engineer, QA, and release engineer, all running from the terminal.His open-source setup, gstack, helped him ship ~10K lines of code and 100 PRs per week for 50 straight days. In this 4-hour hands-on workshop, you'll install his exact setup, run every skill end-to-end, and leave with a production-ready AI development workflow you can use Monday morning.

Stop prompting like a junior dev. Start directing Claude Code like a CEO.

Install and configure Garry Tan's exact Claude Code setup in minutes

Run a CEO-level product review before writing a single line of code

Architect and de-risk features with AI-powered engineering review

Visually QA your work in ~200ms without leaving the terminal

Ship a tested PR from idea to merge in a single session

Run structured retros that make you faster every week

A Claude Pro ($20/mo) or Claude Max subscription with Claude Code access

Claude Pro Subscription

Basic comfort with the terminal (cd, ls, git basics)

Basic terminal experience

A project or repo you want to work on during the workshop (side project, work feature, or starter template we'll provide)

A project you have or want to do

PMs, founders &amp; CEOs who want to ship features themselves. Get a system that bakes product thinking into every build, not just code volume.

Non-technical builders who want to go from "it wrote some code" to shipping tested features end-to-end.

Engineers &amp; developers who use Claude Code but lack a structured workflow. Stop writing ad-hoc prompts — start directing like an eng lead.

"All PMs will be AI PMs in the future" This course is designed for product managers who are working with AI, whether you're just entering the space or already embedded in it: identifying the right problems, influencing technical teams, communicating tradeoffs, and building strategies that matter. In AI, most product managers are figuring things out as they go. The tech is fast-moving. The org is vague. The stakeholders have opinions but little clarity. This course won’t teach you how to train a model, but it will show you how to lead in this environment. You’ll leave with frameworks, mental models, and tactical tools for thinking clearly, prioritizing well, and guiding your teams with confidence. It’s based on what actually happens inside companies like Google, Meta, Amazon, and high-growth AI startups, not theory. The course is structured for people with limited time but high standards. This is not a beginner course. It's for people already in PM or technical roles who want to develop better judgment and become the person others trust to lead. The format is live and cohort-based, so you’ll get feedback from instructors and your peers. You’ll build your thinking muscle in real-time and walk away with a playbook you’ll use again and again. Expect candid discussions, clear examples, and rigorous critique. This course teaches practical skills: how to lead with clarity, prioritize under uncertainty, and develop durable product instincts in the face of complexity. Whether you're working with foundation models, building LLM-based features, or supporting infra teams, the same principles apply: sharpen your thinking, build alignment, and lead decisively. Learning outcomes:• Understand the distinct role of a PM in AI product development• Learn how to make prioritization decisions when outcomes are uncertain• Build stronger relationships with ML, data, and research teams• Learn how to avoid common failure modes in AI product work• Gain tools for storytelling, influencing, and aligning execs around an AI strategy• Navigate technical tradeoffs with clarity even if you don’t write code• Apply product thinking to novel, fast-changing problem spaces

Become a top 1% PM at your company in the AI era

Course syllabus

If your events are recurring and at the same time, it might be easiest to use a single line item to communicate your course schedule to students

Tuesdays & Thursdays

Feel free to type out dates as your title as a way to communicate information about specific live sessions or other events.

May 7, 2022

Schedule items can also be used to convey commitments outside of specific time slots (like weekly projects or daily office hours).

Weekly projects

Course schedule

Experienced PMs: Looking to pivot into AI or currently working with ML teams and seeking to enhance strategic and technical capabilities.

Mid-career AI PMs: Aiming to refine leadership skills, strategic thinking, and gain recognition for their contributions in AI product work.

Founders: Leading AI product efforts and in need of structured decision-making frameworks and strategic guidance.

Aspiring AI PMs: Individuals with a background in software engineering or data science aiming to pivot into AI product management roles.

C-level Executives: Seeking to understand the fundamentals of AI product management to effectively staff and lead AI-focused teams.

Who is this course for

Topics covered

How to reduce noise, recognize patterns, and avoid false certainty when there’s no obvious right answer.

Decision-making under ambiguity

Understand how to validate timing, market readiness, and defensibility for AI initiatives.

The "Why Now" for AI products

Learn the dynamics between model teams and product teams, and how to bridge goals across them.

Working with LLM teams (infra vs applied)

Use language, context, and clarity to get buy-in from both technical and business leadership.

How to earn trust from researchers & execs

Clarify roles in ambiguous teams and avoid stepping on toes while taking ownership.

PM vs TL vs EM roles in AI orgs

Design product strategies that are resilient to model drift, infra shifts, and competitive pressure.

Product strategy: constraints, moats, & goals

Craft compelling, simple stories that help teams and stakeholders understand what you’re doing—and why it matters.

Building narratives that get buy-in

Decide what matters and avoid shipping features just because they're possible.

Leading prioritization without falling into the “build trap”

Lead cross-functionally without getting bogged down in internal debates, confusion, or fear of stepping up.

Handling org politics while staying outcome-focused

A career guide for AI PMs who want to build great products (and get recognized for it).

Learn directly from AI Product Hub

AI Product Hub

Courses

AI Evals for PMs Certification

Claude Code with Garry Tan's 'gstack' Masterclass [4h]

AI Product Playbook