Build your own AI Agents & Crack AI Product Sense, Execution Interviews
Mahesh Yadav
AI Lead-built early Agents @Google, Meta
This course is popular
50+ people enrolled last week.
10x your output with tools like Claude Code & n8n and get AI-PM job-ready
After 300+ interviews and helping 100+ PMs break into AI roles in the past 3 years, I can say this confidently: getting into AI PM is harder than ever — not because companies don’t need AI PMs, but because they now expect PMs who can think, evaluate, and build with AI. Most PMs are still stuck at AI 101.
What’s not working today:
PMs rely on slides instead of shipping with tools like Claude Code or n8n. They talk AI but can’t prototype. They know user flows but not context windows, evals, or how to de-risk ideas before engineering. And because AI shifts every 3–6 months, they can’t tell what’s real vs. hype — so interviews fall flat.
This program fixes that.
After 10+ cohorts, we’ve distilled everything into a 21-day bootcamp that takes you from “I get AI concepts” to “I can build AI agents, evaluate them, and think like an AI PM.” You’ll vibe-code your own agent, create an AI-ready portfolio, master evaluation-first product thinking, and practice interview loops used by top AI teams.
By the end, you won’t just explain AI — you’ll show it with working prototypes, sharper judgment, and the confidence to walk into any AI PM interview like someone who actually builds.
What you’ll learn
Build job-ready AI prototypes to crack AI PM roles with CVs that pass ATS & recruiter filters by highlighting technical & building skills
Use the latest tools like Claude Code, Cursor & n8n to turn ideas into shipped features faster & automate workflows end‑to‑end
Apply product management frameworks from the GenAI PM program to design, scope, & iterate on your co-pilot as a professional-level project
Leverage GenAI PM's best practices to document insights, track outcomes & translate your co-pilot experience into a compelling case study
Use a structured case study template to explain complex build vs buy AI agent decisions, comparing ROI, risk, & strategic fit
Apply a GTM experiment template to launch your AI agent with real customers, capture feedback loops, refine positioning, & generate revenue
Define context-aware logging & red‑teaming requirements, so your AI agents remain observable, auditable & resilient
Transform your course portfolio into an ATS‑optimized CV that surfaces in searches
Showcase shipped agents and co‑pilots, and help your CV stand out in recruiter shortlists
Use your projects to prove you can scope, build, and launch AI features end‑to‑end, making it easier for hiring managers
Practice AI PM interviews 1:1 with internal experts who’ve actually shipped AI products, so you stop guessing what “good” looks like
Get five detailed, written reviews from Mahesh’s team that break down your product sense, execution, and AI depth, with concrete edits
Replace low-signal peer mocks with focused drills on real AI PM questions agent design, evaluation, safety & metrics
Learn directly from Mahesh
Mahesh Yadav
20+ years exp., Ex-Google, Meta Research, AWS AI | 10k+ AI Alums
Who this course is for
Traditional PMs transitioning into AI roles
Professionals from ML/analytics backgrounds trying to reposition for AI-first roles
Engineers or builders pivoting into AI Product
What's included
Live sessions
Learn directly from Mahesh Yadav 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.
Learn minimum 2 AI Tools
Great to have in your portfolio to showcase your practical learning
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
13
Bootcamp Session 1
Dec
14
Bootcamp Session 2
Week 2
Dec
21
Bootcamp Session 3
Free resource
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Context Engineering Challenges & Tips
Core Challenges
Understand common pitfalls in context engineering, from data quality issues to model hallucinations.
Practical Tips
Learn proven strategies PMs can use to design prompts, manage context windows, and guide LLM outputs.
Product Impact
See how strong context engineering leads to better accuracy, usability, and trust in AI products.
Schedule
Live sessions
5 hrs / week
Sat, Dec 13
4:00 PM—9:00 PM (UTC)
Sun, Dec 14
4:00 PM—9:00 PM (UTC)
Sun, Dec 21
4:00 PM—9:00 PM (UTC)
Projects
2 hrs / week
Async content
2 hrs / week
Frequently asked questions
$1,500
USD
2 cohorts