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AI Product Manager -- The Complete Roadmap (0 to 1)

Your one-stop, structured guide to becoming an AI Product Manager -- from fundamentals to cracking interviews.

Interactive Roadmap

graph TD
    START["START HERE
    AI PM Roadmap"] --> PHASE1

    %% ── PHASE 1 ─────────────────────────────────────────
    PHASE1["PHASE 1
    Foundations
    Weeks 1-3"]
    PHASE1 --> F1["Understand What a PM Does
    Read foundational articles"]
    PHASE1 --> F2["Learn Product Thinking
    Frameworks & Mental Models"]
    PHASE1 --> F3["AI / ML Fundamentals
    Courses & Concepts"]

    F1 --> F1a["Product Mgmt: A Complete Definition
    productcoalition.com"]
    F1 --> F1b["How Does a Successful PM Think?
    hackernoon.com"]
    F1 --> F1c["What is a PM Actually?
    medium.com"]

    F2 --> F2a["Product Lifecycle
    Discovery - Delivery - Growth"]
    F2 --> F2b["User Research & Empathy
    Jobs-To-Be-Done Framework"]
    F2 --> F2c["PRDs, Roadmapping &
    Stakeholder Communication"]

    F3 --> F3a["AI for Everyone
    deeplearning.ai - FREE"]
    F3 --> F3b["ML Foundations for PMs
    Coursera / Duke - FREE audit"]
    F3 --> F3c["LLMs, RAG, Embeddings
    Fine-tuning Basics"]

    %% ── PHASE 2 ─────────────────────────────────────────
    F1a & F1b & F1c --> PHASE2
    F2a & F2b & F2c --> PHASE2
    F3a & F3b & F3c --> PHASE2

    PHASE2["PHASE 2
    AI-Specific PM Skills
    Weeks 4-8"]
    PHASE2 --> S1["Prompt Engineering
    & AI Discovery"]
    PHASE2 --> S2["AI Evaluation Frameworks
    Metrics & LLM-as-Judge"]
    PHASE2 --> S3["Data Strategy &
    Model Management"]
    PHASE2 --> S4["AI Ethics, Bias
    & Safety"]

    S1 --> S1a["Build AI Prototypes
    Hands-on Projects"]
    S2 --> S2a["Precision vs Recall
    A/B Testing for AI"]
    S3 --> S3a["Data Pipelines
    Model Drift & Monitoring"]
    S4 --> S4a["Responsible AI
    Fairness & Compliance"]

    %% ── PHASE 3 ─────────────────────────────────────────
    S1a & S2a & S3a & S4a --> PHASE3

    PHASE3["PHASE 3
    Problem Solving & Frameworks
    Weeks 9-12"]
    PHASE3 --> P1["Root Cause Analysis
    5 Whys & RCA Frameworks"]
    PHASE3 --> P2["Product Design Cases
    User-centric Design"]
    PHASE3 --> P3["Guesstimates &
    Market Sizing"]
    PHASE3 --> P4["GTM Strategy
    & Pricing"]

    P1 --> P1a["Funnel Analysis
    Metric Diagnosis"]
    P2 --> P2a["Product Design Approach
    YouTube Tutorials"]
    P3 --> P3a["Guesstimate Frameworks
    Practice Problems"]
    P4 --> P4a["Go-To-Market Cases
    Pricing Strategy"]

    %% ── PHASE 4 ─────────────────────────────────────────
    P1a & P2a & P3a & P4a --> PHASE4

    PHASE4["PHASE 4
    Interview Preparation
    Weeks 12-16"]
    PHASE4 --> I1["Books & Core Prep
    Cracking the PM Interview
    Decode & Conquer"]
    PHASE4 --> I2["Mock Interviews
    Exponent & Practice"]
    PHASE4 --> I3["AI PM Specific Interviews
    Product Sense + AI Technical"]
    PHASE4 --> I4["Company Research
    Portfolio & Storytelling"]

    I1 & I2 & I3 & I4 --> OFFER["OFFER IN HAND"]

    %% ── CONTINUOUS ─────────────────────────────────────────
    PHASE1 -.-> CONT["CONTINUOUS LEARNING
    Throughout the Journey"]
    CONT --> C1["Podcasts
    Lenny's, How I AI
    Product Experience"]
    CONT --> C2["Newsletters
    GenAI PM, Lenny's
    AI PM Guru"]
    CONT --> C3["Communities
    Mind the Product
    Product School"]
    CONT --> C4["Build Side Projects
    Ship AI Features
    Portfolio"]

    %% ── STYLING ─────────────────────────────────────────
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    classDef skill fill:#16213e,stroke:#0f3460,color:#fff,stroke-width:2px
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    class START start
    class PHASE1,PHASE2,PHASE3,PHASE4 phase
    class F1,F2,F3,S1,S2,S3,S4,P1,P2,P3,P4,I1,I2,I3,I4 skill
    class F1a,F1b,F1c,F2a,F2b,F2c,F3a,F3b,F3c,S1a,S2a,S3a,S4a,P1a,P2a,P3a,P4a resource
    class CONT,C1,C2,C3,C4 continuous
    class OFFER goal
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Detailed Roadmap Breakdown

graph LR
    subgraph Timeline["16-WEEK JOURNEY"]
        direction LR
        W1["Weeks 1-3
        FOUNDATIONS"] --> W2["Weeks 4-8
        AI SKILLS"] --> W3["Weeks 9-12
        PROBLEM SOLVING"] --> W4["Weeks 12-16
        INTERVIEWS"]
    end

    classDef timeline fill:#1a1a2e,stroke:#e94560,color:#fff,stroke-width:2px,font-weight:bold
    class W1,W2,W3,W4 timeline
Loading

Table of Contents

Phase 1: Foundations (Weeks 1-3)

Goal: Understand what product management is, how PMs think, and build foundational AI/ML literacy.

What is Product Management?

Resource Link Why Read It
Product Management -- A Complete Definition productcoalition.com The most comprehensive definition of PM -- covers lifecycle, stakeholders, and responsibilities
How Does a Successful PM Think & Act? hackernoon.com Understand the mindset behind great PMs -- strategic thinking, customer obsession
What is a Product Manager Actually? medium.com Cuts through the fluff -- what PMs actually do day-to-day
Shared Google Drive -- PM Resources Google Drive Folder Curated collection of PM templates, frameworks, and study materials

Product Thinking Fundamentals

Learn these core frameworks:

  • Jobs-To-Be-Done (JTBD): Understand why users hire your product
  • Product Lifecycle: Discovery -> Definition -> Development -> Delivery -> Growth
  • User Research: Qualitative (interviews, usability tests) + Quantitative (analytics, A/B tests)
  • PRDs & Roadmapping: Writing specs that engineers love; prioritization (RICE, MoSCoW)
  • Stakeholder Communication: Managing up, down, and across

AI / ML Fundamentals for PMs

You don't need to code models, but you must speak the language fluently:

Concept What to Know
Supervised Learning Classification & Regression -- labeled data in, predictions out
Unsupervised Learning Clustering & pattern discovery -- no labels needed
LLMs (Large Language Models) How GPT/Claude/Gemini work at a high level; tokens, context windows
RAG (Retrieval-Augmented Generation) Combining search + generation for grounded AI responses
Fine-tuning When and why to customize a base model on your data
Embeddings Vector representations of text/images for similarity search
Prompt Engineering Crafting instructions that get reliable outputs from AI
Model Evaluation Precision, recall, F1, hallucination rates, LLM-as-Judge

Phase 2: AI-Specific PM Skills (Weeks 4-8)

Goal: Develop skills unique to AI product management -- managing probabilistic systems, data strategy, and responsible AI.

The AI PM Trinity

graph TD
    AIPM["AI Product
    Manager"] --> DS["Data Strategy"]
    AIPM --> MM["Model Management"]
    AIPM --> UX["User Experience"]

    DS --> DS1["Data Acquisition & Labeling"]
    DS --> DS2["Data Quality & Governance"]
    DS --> DS3["Ethics & Compliance"]

    MM --> MM1["Problem Framing"]
    MM --> MM2["Success Metrics Definition"]
    MM --> MM3["Precision vs Recall Tradeoffs"]

    UX --> UX1["Managing Uncertainty"]
    UX --> UX2["Building Feedback Loops"]
    UX --> UX3["Explaining AI Decisions"]

    classDef center fill:#e94560,stroke:#c0392b,color:#fff,stroke-width:3px,font-weight:bold
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    class AIPM center
    class DS,MM,UX pillar
    class DS1,DS2,DS3,MM1,MM2,MM3,UX1,UX2,UX3 detail
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Key Differences: AI PM vs Traditional PM

Aspect Traditional PM AI PM
Logic Deterministic (if X, then Y) Probabilistic (X likely leads to Y)
Testing Binary pass/fail Accuracy thresholds, edge case analysis
User Expectations Consistent behavior Must manage uncertainty in outputs
Data Feature requirement Core product ingredient
Iteration Ship features Retrain models, tune prompts, expand data
Metrics CTR, conversion, retention + Precision, recall, hallucination rate, latency

What to Build (Portfolio Projects)

  • Build an AI-powered feature using an LLM API (e.g., summarizer, classifier, chatbot)
  • Write an AI PRD with model requirements, data needs, and evaluation criteria
  • Create an AI evaluation framework for a real product
  • Design a feedback loop system for continuous model improvement

Phase 3: Problem Solving & Case Frameworks (Weeks 9-12)

Goal: Master the structured thinking frameworks that every PM interview demands.

RCA -- How to Solve Root Cause Analysis

Root Cause Analysis is one of the most critical PM skills. When a metric drops, you need a systematic approach -- not guesswork.

The RCA Framework

graph TD
    ALERT["Metric Alert
    'DAU dropped 15%'"] --> CLARIFY["1. CLARIFY
    Which metric? By how much?
    Since when? Which segments?"]

    CLARIFY --> INTERNAL["2. CHECK INTERNAL FACTORS"]
    CLARIFY --> EXTERNAL["3. CHECK EXTERNAL FACTORS"]

    INTERNAL --> I1["Recent deployments / bugs?"]
    INTERNAL --> I2["Feature changes / A-B tests?"]
    INTERNAL --> I3["Infrastructure / performance issues?"]
    INTERNAL --> I4["Marketing campaigns paused?"]

    EXTERNAL --> E1["Competitor launches?"]
    EXTERNAL --> E2["Seasonality / holidays?"]
    EXTERNAL --> E3["Market / regulatory changes?"]
    EXTERNAL --> E4["Platform policy changes?"]

    I1 & I2 & I3 & I4 & E1 & E2 & E3 & E4 --> SEGMENT["4. SEGMENT ANALYSIS
    Break down by platform, geo,
    user cohort, device, channel"]

    SEGMENT --> FUNNEL["5. FUNNEL ANALYSIS
    Where in the journey
    is the drop happening?"]

    FUNNEL --> ROOT["6. IDENTIFY ROOT CAUSE
    Apply 5 Whys to the
    most impacted segment"]

    ROOT --> ACTION["7. ACTION PLAN
    Fix, monitor, prevent recurrence"]

    classDef alert fill:#e74c3c,stroke:#c0392b,color:#fff,stroke-width:2px,font-weight:bold
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    class ALERT alert
    class CLARIFY,SEGMENT,FUNNEL,ROOT step
    class INTERNAL,EXTERNAL detail
    class I1,I2,I3,I4,E1,E2,E3,E4 detail
    class ACTION action
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The 5 Whys Technique

Step Example
Problem App DAU dropped 15% this week
Why 1? Sign-ups from paid channels dropped 40%
Why 2? Google Ads campaigns were paused
Why 3? Monthly budget was exhausted on Day 22
Why 4? CPC increased 60% due to competitor bidding
Why 5? No automated bid caps or budget alerts were set
Root Cause Missing budget monitoring automation
Fix Implement automated bid caps + budget alerts + weekly spend reviews

RCA Tips for Interviews

  1. Always start with absolute numbers before diving into percentages
  2. Segment early -- the aggregate hides the story
  3. Check the obvious first -- bugs, deployments, outages
  4. Distinguish correlation from causation -- just because two things changed doesn't mean one caused the other
  5. Propose short-term fix + long-term prevention

RCA Resources

Product Design Cases Approach

Product design questions ask you to design a product or feature for a specific user problem.

The Design Framework

graph TD
    Q["PRODUCT DESIGN QUESTION
    'Design X for Y'"] --> C["1. CLARIFY & CONTEXT
    Who is the user?
    What's the goal?
    What platform?"]

    C --> U["2. USER SEGMENTS
    Identify 2-3 user types
    Pick one to focus on"]

    U --> P["3. PAIN POINTS
    List 3-5 pain points
    for chosen user segment"]

    P --> S["4. SOLUTIONS
    Brainstorm solutions
    for top pain points"]

    S --> PR["5. PRIORITIZE
    Impact vs Effort
    Pick 1-2 features"]

    PR --> D["6. DETAIL THE SOLUTION
    User flow, edge cases,
    key screens / interactions"]

    D --> M["7. METRICS
    How will you measure
    success? North Star + guardrails"]

    classDef question fill:#e74c3c,stroke:#c0392b,color:#fff,stroke-width:2px,font-weight:bold
    classDef step fill:#1a1a2e,stroke:#e94560,color:#fff,stroke-width:2px

    class Q question
    class C,U,P,S,PR,D,M step
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Design Case Resources

Resource Link Type
Product Design Cases Approach YouTube Video walkthrough
How to Answer Product Design Questions (2025) YouTube Interview framework
Product Design Whiteboarding -- Netflix PM YouTube Mock interview
PCA Framework for Product Sense YouTube Structured method

Guesstimates & Market Sizing

Guesstimates test your ability to break down ambiguous problems into logical, quantifiable components.

The Guesstimate Framework

graph TD
    Q["GUESSTIMATE QUESTION
    'How many X in Y?'"] --> C["1. CLARIFY SCOPE
    Geography? Time period?
    Define X precisely"]

    C --> A["2. TOP-DOWN or BOTTOM-UP?
    Choose your approach"]

    A --> TD["TOP-DOWN
    Start with population
    Filter down with %"]

    A --> BU["BOTTOM-UP
    Start with unit economics
    Scale up"]

    TD --> B["3. BUILD THE TREE
    Break into components
    Estimate each branch"]

    BU --> B

    B --> S["4. SANITY CHECK
    Does the final number
    make intuitive sense?"]

    S --> R["5. STATE ASSUMPTIONS
    Be transparent about
    what you assumed"]

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    class Q question
    class C,B,S,R step
    class A,TD,BU approach
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Guesstimate Resources

Resource Link Type
How to Solve Guesstimates YouTube Video walkthrough
[optional] Case Interviews Cracked (Book) -- Guesstimates & Profitability chapters

GTM Strategy & Pricing

GTM Resources

Resource Link Type
GTM Strategy Questions YouTube GTM case walkthrough
Pricing Strategy YouTube Pricing frameworks
Interviewer-Led Strategy Case (Microsoft/Amazon PM) YouTube Real mock interview

Phase 4: Interview Preparation (Weeks 12-16)

Goal: Combine everything you've learned into crisp, structured interview answers.

Interview Types for AI PMs

graph TD
    INT["AI PM
    INTERVIEW"] --> PS["Product Sense
    & Design"]
    INT --> AN["Analytical
    & Metrics"]
    INT --> AI["AI Technical
    Depth"]
    INT --> ST["Strategy
    & Vision"]
    INT --> BH["Behavioral
    & Leadership"]
    INT --> EX["Execution
    & Prioritization"]

    PS --> PS1["Design an AI feature"]
    PS --> PS2["Improve an existing product"]
    PS --> PS3["Product for a new market"]

    AN --> AN1["Metric diagnosis / RCA"]
    AN --> AN2["Guesstimates"]
    AN --> AN3["A/B test design"]

    AI --> AI1["When to use AI vs rules?"]
    AI --> AI2["How to handle hallucinations?"]
    AI --> AI3["Data strategy for ML"]

    ST --> ST1["GTM for AI product"]
    ST --> ST2["Build vs buy vs API"]
    ST --> ST3["AI pricing strategy"]

    BH --> BH1["Tell me about a time..."]
    BH --> BH2["Conflict with eng/design"]
    BH --> BH3["Failed project learnings"]

    EX --> EX1["Prioritization frameworks"]
    EX --> EX2["Roadmap tradeoffs"]
    EX --> EX3["Cross-functional alignment"]

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    class PS,AN,AI,ST,BH,EX type
    class PS1,PS2,PS3,AN1,AN2,AN3,AI1,AI2,AI3,ST1,ST2,ST3,BH1,BH2,BH3,EX1,EX2,EX3 example
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AI PM Interview Evaluation Criteria (2025-26)

Companies now evaluate AI PMs on six core competencies:

# Competency What They Look For
1 Technical Skills & Building GitHub activity, personal AI projects, hands-on prototyping
2 Product Thinking & 0-to-1 Can you go from ambiguity to a clear product vision?
3 AI/ML Knowledge Deep intuition, not just surface-level vocabulary
4 Product Sense & Judgment User empathy, taste, knowing what to build and what NOT to
5 Communication & Collaboration Can you align eng, design, data science, leadership?
6 Execution & Metrics Track record of shipping, measuring, and iterating

Key Insight: Companies increasingly hire people who can build AI products themselves, not just manage them. Show evidence of building.

Books -- The Essential Reading List

Primary Interview Prep (Must Read)

Book Key Chapters Focus Area
Cracking the PM Interview -- Gayle Laakmann McDowell Ch 13, 14, 15 (primary), Ch 16 Product design, estimation, behavioral, technical questions
Decode & Conquer -- Lewis Lin Ch 1, 2, 3, 4, 5, 8, 10 CIRCLES method, product design, metrics, estimation
Preparing for Product Interviews -- Advaith Sridhar All chapters Written by a Flipkart APM alum -- practical, India-focused

Optional / Deep Dives

Book Focus Area
Case Interviews Cracked Guesstimates & Profitability cases
Inspired -- Marty Cagan How top tech companies build products
The Lean Product Playbook -- Dan Olsen Product-market fit, MVP, lean methodology
AI Product Management -- Irene Bratsis AI-specific PM frameworks and case studies
Machine Learning Yearning -- Andrew Ng Free book on developing ML algorithms (great for AI PM context)

Podcasts

Listen during commutes, gym sessions, or walks. These are the best for AI PM learning.

Podcast Host(s) Why Listen Link
Lenny's Podcast Lenny Rachitsky #1 PM podcast -- features top product leaders from FAANG+ lennyspodcast.com / Apple / Spotify
How I AI Claire Vo (via Lenny's) Practical AI usage -- 30-min episodes with live demos lennysnewsletter.com
The Product Experience Lily Smith & Randy Silver Mind the Product's podcast -- deep dives with senior PMs mindtheproduct.com / Spotify
This is Product Management Mike Fishbein 125+ episodes with PMs from Adobe, Spotify, Shopify thisPM.com
The Product Podcast Product School Weekly, featuring PMs from Google, Meta, Amazon productschool.com
AI Product Strategy (Lenny x Intercom) Lenny + Paul Adams AI's impact on product strategy from Intercom's CPO Episode Link

Newsletters & Communities

Newsletters (Subscribe to All -- They're Free)

Newsletter Focus Link
GenAI PM Daily/weekly AI-curated PM briefings from 1000+ sources genaipm.com
Lenny's Newsletter Product management, growth, career -- the gold standard lennysnewsletter.com
AI Product Management Guru Technical depth on AI architecture, chatbot systems, AI lifecycle Substack
AI Product Management (Angus Allan) News at the intersection of AI and PM Substack
The Product Compass Frameworks, templates, and practical PM advice Substack
Aakash Gupta's Newsletter AI PM transition guides, career advice, deep dives aakashg.com

Communities

Community Platform Link
Mind the Product Website + Slack mindtheproduct.com
Product School Community Website + Events productschool.com
Lenny's Community Paid Slack community lennysnewsletter.com
r/ProductManagement Reddit reddit.com/r/ProductManagement
PMExercises Practice platform productmanagementexercises.com

YouTube Channels & Playlists

Must-Watch Videos (Curated for This Roadmap)

Video Topic Link
PM Interview Prep Playlist RCA, Cases, Product Sense YouTube Playlist
Product Design Cases Approach How to structure design answers YouTube
How to Solve Guesstimates Estimation frameworks & practice YouTube
GTM Strategy Questions Go-to-market case walkthroughs YouTube
Pricing Strategy Pricing frameworks for PM interviews YouTube
AI PM Complete Course 0-to-1 AI PM crash course YouTube
How to Manage AI Risks -- Exponent AI safety mock interview YouTube
Product Design Whiteboarding (Netflix) Live mock interview YouTube
PCA Framework for Product Sense Structured answer framework YouTube
Interviewer-Led Case (Microsoft/Amazon) Business strategy mock YouTube

Channels to Subscribe To

Channel Focus
Exponent PM mock interviews, frameworks, courses
Product School Talks from FAANG PMs, product management fundamentals
Lenny's Podcast Video versions of top PM podcast episodes

Free Courses & Certifications

Course Provider Duration Link
AI for Everyone DeepLearning.AI (Andrew Ng) ~6 hours deeplearning.ai
ML Foundations for Product Managers Duke / Coursera ~20 hours Coursera
Building AI-Powered Products IBM / Coursera ~7 hours Coursera
AI Product Manager Nanodegree Udacity ~18 hours Udacity (Paid)
The Complete AI PM Learning Path Aakash Gupta (Free) Self-paced Medium
AI PM Transition Guide (2025) Aakash Gupta Self-paced aakashg.com
How to Build Your Career in AI Andrew Ng (Free PDF) ~1 hour read deeplearning.ai
Machine Learning Yearning Andrew Ng (Free PDF) ~2 hour read deeplearning.ai

Tools Every AI PM Should Know

Category Tools
AI Prototyping ChatGPT, Claude, Gemini, Cursor, v0.dev
Product Analytics Amplitude, Mixpanel, PostHog, FullStory
Roadmapping Linear, Productboard, Notion, Jira
Design Figma, Whimsical, FigJam
Data & Experiments Statsig, LaunchDarkly, Optimizely
AI Evaluation LangSmith, Braintrust, Humanloop
Documentation Notion, Confluence, Coda
Communication Loom, Slack, Miro

Interview Question Types for AI PMs

Sample Questions to Practice

Product Sense & Design (click to expand)
  1. Design an AI-powered feature for Instagram that helps creators grow their audience.
  2. How would you improve Google Translate using AI?
  3. Design a personalized learning assistant for Khan Academy.
  4. Should Spotify use AI to generate podcast summaries? How would you design it?
  5. Design an AI tool that helps doctors with diagnosis.
Analytical / RCA / Metrics (click to expand)
  1. YouTube watch time dropped 10% week-over-week. Diagnose the issue.
  2. Uber ride completions are down 5% in NYC. What happened?
  3. How would you measure success for ChatGPT's new memory feature?
  4. Design an A/B test for a new AI-powered search feature.
  5. What metrics would you track for an AI customer support chatbot?
AI Technical Depth (click to expand)
  1. When should you use RAG vs fine-tuning for a customer support bot?
  2. How would you handle hallucinations in a healthcare AI product?
  3. What's your approach to building an AI evaluation framework?
  4. How do you decide between using an LLM API vs training your own model?
  5. Explain the tradeoffs of using AI for content moderation.
Strategy & GTM (click to expand)
  1. You're launching an AI writing assistant. Design the GTM strategy.
  2. How would you price an AI-powered analytics tool for SMBs?
  3. Should a startup build their own LLM or use OpenAI's API?
  4. A competitor just launched a similar AI feature. What's your response?
  5. How would you prioritize AI investments across a product portfolio?
Behavioral (click to expand)
  1. Tell me about a time you had to make a decision with incomplete data.
  2. Describe a situation where you disagreed with engineering on a technical approach.
  3. How have you handled a product launch that didn't meet expectations?
  4. Tell me about a time you had to sunset a feature.
  5. How do you build alignment across cross-functional teams?

Weekly Study Plan (16-Week Template)

Week Focus Activities
1 What is PM? Read 3 foundational articles, subscribe to newsletters
2 Product Thinking Study JTBD, user research, PRD writing
3 AI/ML Basics Complete "AI for Everyone", study key AI concepts
4 AI for PMs Start ML Foundations course, learn LLM concepts
5 AI PM Skills Study AI PM Trinity, write an AI PRD
6 Data & Ethics Learn data strategy, responsible AI, bias frameworks
7 Build Something Create an AI prototype, document your process
8 AI Evaluation Learn metrics, evaluation frameworks, A/B testing for AI
9 RCA & Metrics Practice 5 RCA problems, study metric frameworks
10 Product Design Cases Practice 5 design cases using the framework
11 Guesstimates & GTM Practice 5 guesstimates, study GTM frameworks
12 Book Deep Dive Read key chapters from Cracking PM & Decode & Conquer
13 Mock Interviews Do 3 mock interviews (use Exponent or peer practice)
14 AI-Specific Prep Practice AI product sense, technical depth questions
15 Behavioral Stories Prepare 8-10 STAR stories covering key competencies
16 Final Review Full mock interview, refine weak areas, rest before interviews

Quick Reference Card

THE AI PM FORMULA
=================

Product Sense + AI Technical Fluency + Structured Thinking = AI PM

INTERVIEW ANSWER STRUCTURE
===========================
1. Clarify (ask questions, define scope)
2. Structure (state your framework)
3. Execute (walk through methodically)
4. Summarize (recap key points, state recommendation)

RCA IN 60 SECONDS
==================
Metric dropped? -> Clarify scope -> Segment data -> Check internal
(bugs, releases, campaigns) -> Check external (competitors, seasonality)
-> Funnel analysis -> 5 Whys -> Root cause -> Fix + Prevent

DESIGN CASE IN 60 SECONDS
===========================
Clarify -> Users -> Pain Points -> Solutions -> Prioritize -> Detail -> Metrics

GUESSTIMATE IN 60 SECONDS
===========================
Clarify -> Top-down or Bottom-up -> Break into components -> Estimate
each -> Multiply -> Sanity check -> State assumptions

Contributing

Found a broken link? Have a great resource to add? Open a PR or raise an issue.

Remember: The best PMs are not just frameworks machines -- they are deeply curious, user-obsessed, and bias-to-action builders. Learn the frameworks, then transcend them.

Good luck on your AI PM journey!

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