AI PM Interview Kit

Practice the question types that are actually showing up in AI PM interviews.

This repo is a focused interview-prep system for experienced product managers moving into AI PM roles. It is built for people who already know general PM frameworks but need role-specific practice on AI product design, evaluation, prioritization, model tradeoffs, and senior-level behavioral storytelling.

Who this is for

• PMs transitioning into AI PM roles
• Senior PMs preparing for AI-focused interviews
• Candidates who want a practical practice loop, not another generic PM reading list

What is included

• behavioral-prompts.md — structured reps for leadership, failure, conflict, and judgment stories
• product-design-prompts.md — AI product-sense questions with trust and workflow framing
• prioritization-prompts.md — decision-making under uncertainty, constraints, and competing bets
• ai-tradeoffs-prompts.md — model, architecture, latency, cost, and trust tradeoff practice
• metrics-prompts.md — success metrics, eval-to-KPI thinking, and guardrails
• real-questions.md — 20 real AI PM interview questions sourced from Glassdoor, Blind, and LinkedIn writeups (2024-2026), organized by question type with company tags and notes on why each question is hard
• study-plan.md — 7-day prep schedule that sequences all files into a daily rep cadence

How to use this repo

1. Pick one file based on the interview loop you are preparing for.
2. Paste one practice prompt into Claude and answer aloud or in writing.
3. Ask Claude to score you using the rubric in the file.
4. Rewrite the answer once. The second rep is where the improvement happens.
5. Save your best answer outlines in a separate prep doc so you build a personal answer bank before interviews.

Why this repo should exist

Most PM interview materials are still generic. AI PM interviews are not. Companies now test whether candidates can:

• scope AI-native product ideas
• define success metrics with trust guardrails
• reason through model and architecture tradeoffs
• prioritize under ambiguity
• explain leadership decisions when quality is probabilistic, not binary

This repo exists to make those reps explicit.

Company Prep

Targeted prep guides for 6 companies actively hiring AI PMs:

Netherlands track (visa-sponsoring)

• Spotify — squad culture, ML recommendations, Amsterdam
• Booking.com — experimentation culture, travel AI, Amsterdam

Global

• Google / Google DeepMind — Gemini, Search AI, structured interviews

India track

• Razorpay — B2B payments infrastructure, technical PM bar
• CRED — premium consumer, behavioral design, trust economy
• Flipkart — e-commerce at scale, seller ecosystem, Tier 2/3 India

Suggested repo structure

Keep the root clean. One file per question type is enough. Add examples only after you have real candidate feedback on which prompts produce the best interview reps.

Positioning line

Built from live 2026 AI PM job-hunt experience, not hindsight summaries.