Walk into any AI PM interview knowing exactly what the scorecard says and how to fill every column
For product managers and aspiring AI PMs who want a job at OpenAI, Anthropic, Meta, Google, or Microsoft and need to master every round before they get on the phone
9 chapters, 63 lessons
14-day refund on the yearly plan. Real pricing on the plans page, no surprises.
63
lessons, yours to run
Easy to pick up, built to get you the next job
No coding background needed, and you write code with Claude as you go.
Every section below turns into something a hiring manager recognises, not just notes.
Know the five columns before you walk in
- You'll decode the exact five-dimension scorecard interviewers fill in and learn which score in which column can sink an otherwise strong loop
- You'll learn to aim every sentence at a specific dimension rather than hoping the interviewer notices you're good
- No technical background assumed: the scorecard lesson is the first thing you watch, and it reframes every answer you give from that point on
- Hiring managers pay for candidates who understand the evaluation rubric, not just the subject matter
Speak AI fluently without a CS degree
- You'll attach a product decision and a real tradeoff to tokens, context window, temperature, and hallucinations so you sound like an engineer's peer, not a passenger
- You'll explain RAG versus fine-tuning versus agents in a way that tells the interviewer you can ship, not just summarise a Wikipedia article
- Every concept is taught through napkin-sketch analogies and worked product scenarios, plain English from start to finish
- Knowing when to use RAG versus fine-tuning is exactly the kind of judgement a hiring manager at an AI company will probe in round two
Structure any answer in real time with reusable frameworks
- You'll apply a seven-step product design framework with an AI overlay that takes you from blank page to a committed recommendation with a metric and a guardrail
- You'll define a North Star metric and a counter-metric for any AI feature so you never say 'we'll track engagement' again
- Frameworks are taught as fill-in templates you can run in your head during a live interview with no notes in front of you
- Being able to structure ambiguous AI problems on the spot is a skill senior hiring managers test for explicitly in product sense rounds
Nail every round from product sense to take-home
- You'll work through the 50 real questions top AI companies actually ask across Product Sense, Technical, Strategy, Execution, Behavioural, and Take-Home rounds
- You'll build a worked answer for take-home and practical exercises so you arrive with a portfolio piece, not just notes
- Each round is treated as a distinct scoring event with its own traps, and you'll learn the specific moves that clear bar in each one
- Candidates who demonstrate command across all six round types are the ones who get offers, not those who ace one round and fade in another
Finish this course and you can do all of this, no prior background required:
- You'll be able to explain the model selection tradeoff between RAG and fine-tuning in a technical round in plain language that engineers and interviewers both respect
- You'll be able to design an AI product feature end to end using a structured framework, naming the ML problem type, the North Star metric, and the guardrail counter-metric
- You'll be able to diagnose a metrics question by running a structured funnel before you guess at a root cause, which is exactly what execution-round interviewers are looking for
- You'll be able to answer safety and failure-mode questions with a concrete approach to hallucination risk, model error costs, and human-in-the-loop design rather than a vague nod to responsibility
- You'll be able to deliver a take-home exercise that reads like the output of someone already doing the job, not someone studying for it
- You'll be able to walk into any of the six interview rounds knowing which scorecard dimension it tests and which specific moves clear bar on that dimension
63 lessons, 9 chapters
Built by practitioners, not influencers
Every agent, skill file, and dataset is drawn from 25+ years and 50+ Oracle ERP implementations across pharma, manufacturing, semiconductor, and distribution.
Kept current with the stack
Claude Code, skills, and MCP move fast. Your licence includes every system update for as long as you're subscribed. Your system gets better, not obsolete.
You can get a phone screen at an AI company but the loop ends at round two or three and the feedback says nothing useful. You know enough to talk about AI products but you do not know what the interviewer is actually writing down while you speak. Every answer you give feels solid until you realise you named a model without a tradeoff, proposed a metric without a guardrail, or skipped straight to solutions without scoping the problem first. The job postings say AI PM experience required, and the rejection emails say you were a strong candidate, and neither tells you which of the five scorecard columns killed your loop.
What you'll be able to do, module by module
Every module leaves you able to build and run something real. Here is the syllabus, in the order you work through it.
Curriculum
9 chapters · 63 lessons
0. Orientation: The Scorecard
Identify the five dimensions interviewers score in every AI PM loop and understand how a single below-bar column sinks an otherwise strong set of answers
The 5-Dimension AI PM Scorecard1 item
- What the Hiring Manager Is Actually Scoring🔒
1. LLM Fundamentals
Attach a concrete product decision and a real tradeoff to tokens, context window, temperature, hallucinations, RAG, fine-tuning, RLHF, evals, inference, and agents so you answer technical questions as a product owner, not a vocabulary reciter
Core AI Concepts Every PM Must Know2 items
- LLM Basics: Tokens, Context Window, Temperature, Hallucinations🔒
- RAG, Fine-Tuning, RLHF/DPO, Evals, Inference, Agents🔒
2. The Reusable Answer Frameworks
Apply a structured seven-step product design method with an AI overlay and define a North Star metric plus a guardrail counter-metric for any AI feature question in real time
Frameworks That Work for AI PM Interviews4 items
- CIRCLES for AI Product Design🔒
- Metrics & Estimation: North Star, Guardrails, Funnels, Diagnosis🔒
- STAR for AI-Flavored Behavioral Stories🔒
- Writing a PRD / Memo / Eval Framework Under Time Pressure🔒
3. Round 1: Product Sense with AI
Walk from a real user problem to a specific, measurable AI solution with a named ML problem type, a prioritised use case, and a stated failure mode
Product Sense Questions & Method10 items
- How to Attack a Product Sense Round🔒
- Q1: Design an AI Product for a Ride-Sharing App🔒
- Q2: You Have Text-to-Music Capabilities. How Would You Productize It?🔒
- Q3: How Would You Improve ChatGPT for Enterprise Users?🔒
- Q4: Design a RAG System for TikTok's Moderation Team🔒
- Q5: Design an AI Feature for Enterprise Users of Claude🔒
- Q6: Prioritize Features for an AI Writing Assistant in Word🔒
- Q7: Design How Users Interact with an AI Scheduling Assistant🔒
- Q8: Design an AI Agent for a Streaming Service🔒
- Q9: A VC Asks You to Build an AI Career Coaching Company🔒
4. Round 2: AI Technical Understanding
Demonstrate the depth of AI technical knowledge a product manager is expected to own, including model selection tradeoffs and the build-versus-buy decisions that come up in every technical round
Technical Questions & Method10 items
- How to Attack a Technical Round🔒
- Q10: Define Hallucinations in LLMs🔒
- Q11: How Do You Handle Hallucinations in Production?🔒
- Q12: What's the Effect of Adjusting Context Window Size?🔒
- Q13: What Metrics Did You Use to Evaluate Your LLM's Performance?🔒
- Q14: What's Your Criteria in Selecting a Model?🔒
- Q15: What's Your Understanding of the RAG Framework?🔒
- Q16: Are You Familiar with RLHF? What Do You Know About DPO?🔒
- Q17: When Do You Use Rule-Based vs. NN vs. LLM Models?🔒
- Q18: How Would You Build an LLM Inference Pipeline?🔒
5. Round 3: AI Strategy & Business
Frame competitive positioning, platform strategy, and monetisation decisions for AI products in a way that shows commercial judgement alongside product thinking
Strategy Questions & Method9 items
- How to Attack a Strategy Round🔒
- Q19: How Do You Approach GenAI Safety in Consumer Products?🔒
- Q20: A Model with 10x Capability at 10x Cost. What Do You Do?🔒
- Q21: What Industry Could Benefit Most from Enterprise ChatGPT?🔒
- Q22: Design Safeguards for an AI That Acts on a User's Behalf🔒
- Q23: How Do You Balance Product Velocity with Safety Constraints?🔒
- Q24: Capability vs. Safety on One Roadmap. Prioritize.🔒
- Q25: Your Long-Term Product Vision for Anthropic, as a Roadmap🔒
- Q26: What Strategies Keep a Product Defensible in an AI-Saturated Market?🔒
6. Round 4: Execution & Metrics
Define launch plans, funnel diagnostics, and metric trees for AI features and explain how you would detect and respond to model degradation in production
Execution Questions & Method10 items
- How to Attack an Execution and Metrics Round🔒
- Q27: What Goal for an AI-Only Social Network OpenAI Is Building?🔒
- Q28: Measure Success for OpenAI. What If Instrumentation Went Down?🔒
- Q29: You Lead the ChatGPT 6 Rollout. How Do You Launch It?🔒
- Q30: Estimate the Number of ChatGPT Users Worldwide🔒
- Q31: What Guardrail Metrics Would You Track? One Flags an Issue. Now What?🔒
- Q32: How Do You Build Customer Trust When Launching AI Features?🔒
- Q33: DAU of a Claude Feature Dropped 15% Last Week. Diagnose It.🔒
- Q34: You're Meta's PM for AI Chat. Define Success and Goals.🔒
- Q35: AI Sends Transcripts to All Invitees; Attendance Drops. What Now?🔒
7. Round 5: Behavioral, AI-Flavored
Structure behavioural stories that prove you can navigate the specific tradeoffs AI PMs face around safety, model error, and cross-functional alignment with research and engineering teams
Behavioral Questions & Method9 items
- How to Attack a Behavioral Round🔒
- Q36: How Have You Adopted AI in Your Workflows?🔒
- Q37: Tell Me About a Time You Built an AI Product from Scratch🔒
- Q38: Describe a Time Your AI Project Failed. What Did You Do?🔒
- Q39: Explain a Complex AI Concept to a Non-Technical Stakeholder🔒
- Q40: Why Anthropic, and What Draws You to AI Safety?🔒
- Q41: What Slightly Unusual Beliefs Do You Hold?🔒
- Q42: A Launched AI Feature Had Real Model Limits. How Did You Design the CX?🔒
- Q43: Models Gave Inconsistent Predictions. How Did You Work with Scientists?🔒
8. Round 6: Take-Home & Practical
Produce a complete take-home submission that demonstrates structured product thinking, AI technical fluency, and measurable success criteria across a realistic brief
Take-Home Questions & Method8 items
- How to Attack a Take-Home and Practical Round🔒
- Q44: Two Written Prompts, a 2-Pager Each, Plus a Panel Presentation🔒
- Q45: Advance Case: A Written Memo or PRD as Round One🔒
- Q46: Whiteboard an Eval Framework for a Generative Feature🔒
- Q47: Prototype a Prompt Chain and Discuss Measuring Its Reliability🔒
- Q48: Walk Through Eval Pipeline Design and Human-in-the-Loop Setup🔒
- Q49: How Do You Evaluate a Prompt? (Live Practical Probe)🔒
- Q50: If You Were Already a PM on This Team, What Would You Do and Why?🔒
A candidate who can name the scoring dimension being tested mid-answer, attach a tradeoff to every technical concept, and produce a take-home that reads like a working spec is not common. Stacking scorecard awareness, AI technical fluency, and structured metrics thinking in one profile is how you become the candidate the hiring team talks about after everyone else has been forgotten.
From subscribing to running your first artifact
Subscribe and unlock all 63 videos
Get immediate access to the full nine-hour course. Start with the Orientation module so you understand the five-dimension scorecard before you watch a single worked answer. No setup, no software, no coding environment to configure.
Work through each round in order
Each module maps to a real interview round. Watch the lesson, study the worked example, then close your laptop and say your own answer out loud. The frameworks are designed to run in your head during a live interview with no notes.
Build your take-home and walk into your loop ready
Complete the Round 6 take-home module and finish with a worked practical exercise you can show as a portfolio piece. You leave with demonstrated capability across every round type, not just a list of things you read about.
How you would answer: Design an AI feature for a customer support product
Comprehend and identify before you design
You open with CIRCLES. Comprehend: this is a B2B SaaS support tool with high ticket volume and a mix of simple policy questions and complex billing disputes. Identify: the primary user is a support agent under time pressure, not the end customer. You state this out loud so the interviewer sees your structure.
Name the ML problem type
You pick one use case rather than listing five. Suggested response draft generation: the model reads the incoming ticket and the relevant policy document via RAG, then generates a reply for the agent to review and send. You name it as a retrieval-augmented generation problem, not just 'AI chat', and you explain why fine-tuning alone would not handle a policy document that changes monthly.
Set the temperature and handle the failure mode
You note that temperature should sit at 0.3 for this use case. The draft needs to be consistent and citable, not creative. You state the hallucination risk: a support agent who sends a confidently wrong refund policy is a liability, so every draft must include the source passage and a confidence threshold below which the model surfaces a flag rather than a draft.
Attach a North Star metric and a guardrail
North Star: agent-accepted draft rate, meaning the proportion of AI drafts the agent sends with no material edit. Guardrail: customer re-contact rate within 48 hours, which catches cases where the draft was accepted but wrong. You explain both in one sentence each so the interviewer hears that you know the difference between a vanity metric and a useful one.
Summarise with a tradeoff
You close by naming the tradeoff you chose: RAG over fine-tuning because the facts change faster than tone does, and a wrong fact is more expensive than a slightly off-brand sentence. One committed recommendation, one stated tradeoff, one metric pair. That is a 4 on metrics rigour and at bar on every other column.
The toolkit you'll build and run in this program
You leave with working assets, not notes.
Real things you can demo in an interview.
A five-dimension scorecard reference you can use to self-assess every practice answer before your real loop
A reusable seven-step AI product design framework with the AI overlay built in, ready to run from memory in a live interview
A worked take-home submission from Round 6 that you can use as a template and portfolio reference
A library of 50 decoded real questions across all six round types with worked answers you can study and adapt
A metrics and estimation playbook covering North Star, guardrails, funnel diagnosis, and stated-assumption estimation
You'll get the most from this if
Product managers currently working in tech who want to move into an AI PM role at a top AI company and know their existing interview prep is not specific enough
Senior PMs and Group PMs who are strong on product sense but need to close the gap on AI technical depth before a loop at a model lab or AI-first company
Career changers with domain experience who understand products but have not yet built the AI vocabulary and framework fluency to clear a technical round
Candidates who have already been through an AI PM loop, got past screening, and want to understand exactly which round and which dimension cost them the offer
Yearly plan: 14-day keep-the-system guarantee
14-day refund on the yearly plan. Finish the first two modules, and if you haven't run your first live pipeline, take a full refund.
Straight answers before you subscribe
I have no coding or engineering background. Will I be lost in the technical modules?
No. Every technical concept in the course is taught through product analogies and worked examples designed for people who have never written a line of code. The LLM Fundamentals module uses a taxi meter to explain tokens and a restaurant order to explain temperature. You are learning to make product decisions using these concepts, not to implement them, and the course is built around that distinction from lesson one.
Does this work if I am interviewing at a company that is not one of the five named in the description?
Yes. The course teaches the underlying scorecard logic and frameworks that AI PM interviews at any serious company use, not company-specific trivia. The 50 questions and the round structure reflect how AI PM interviews are run broadly across the industry. The frameworks you build are reusable across any loop.
What if I watch it all and it does not help me? Is there a refund?
There is a 14-day refund policy. If you have gone through the material and it has not been useful, contact support within 14 days of purchase and you will get your money back.
Will the content go out of date as AI moves fast?
The course is updated when the curriculum warrants it. Because the core content is built around frameworks and scorecard logic rather than specific model releases or feature announcements, the underlying material stays relevant. Updates are included for subscribers.
Can I use the worked examples and frameworks in actual interviews and job applications?
Yes. The frameworks, worked answers, and take-home template are yours to use in real interviews and to adapt for your own portfolio. There are no restrictions on applying what you learn in your own professional context.