A real-world PM case study simulator for aspiring Product Managers — practice structured product thinking through hands-on scenarios with AI-powered feedback on your reasoning.
Most PM prep is passive: read frameworks, watch videos, hope you remember under pressure. This is active. It puts you in actual PM situations — prioritization under constraints, metrics investigation, feature trade-offs, go-to-market execution — and AI feedback tells you exactly where your thinking breaks down.
Built for PM candidates preparing for interviews at FAANG, startups, and growth-stage companies. Also used by hiring teams and recruiters as a screening tool to identify candidates with strong product fundamentals.
The Problem: Traditional PM interview prep relies on passive learning. You read about frameworks, memorize case study structures, but when you're in an actual interview, the pressure and ambiguity overwhelm your preparation.
The Solution: PM Scenario Lab puts you in realistic PM situations with:
- ✅ Concrete constraints (team size, budget, timeline)
- ✅ Ambiguous problems that require clarification
- ✅ AI-powered feedback that surfaces gaps in your reasoning
- ✅ Measurable progress tracking across multiple attempts
- Scenario Library: Real-world PM case studies across product design, feature prioritization, metrics diagnosis, and go-to-market planning
- Structured Response Framework: Guides you through a PM thinking approach — clarify scope → segment users → prioritize → define success metrics → measure
- AI Feedback Engine: Gemini evaluates your response against PM best practices and surfaces gaps in your reasoning — not just right/wrong
- Difficulty Levels: Beginner (framework practice), Intermediate (trade-offs), Advanced (ambiguous, open-ended)
- Built-in Framework Reference: RICE, ICE, HEART, North Star, Jobs-to-be-Done, AARRR, MoSCoW — available in-context while you work
- Weakness Tracking: Surfaces your recurring gaps across sessions so you know exactly what to improve
- Interview-Ready Output: Export your responses and AI feedback as a portfolio piece for interviews
- Select a Scenario — Choose from product design, prioritization, metrics, or GTM challenges
- Work Through the Framework — Answer guided prompts that mirror real PM interview questions
- Get AI Feedback — Receive detailed evaluation on your reasoning, trade-offs, and strategic thinking
- Track Progress — See trends in your strengths and gaps over time
- Interview Confidently — Use your practice scenarios and feedback as real-world examples in interviews
- Product Design — Design a feature, improve a metric, or redesign a flow
- Example: "Redesign Pinterest's search experience to reduce churn among new users"
- Prioritization — Rank a backlog with constrained engineering capacity
- Example: "Your team has 2 sprints. Prioritize these 10 feature requests with limited context"
- Metrics & Diagnosis — DAU dropped 15%, figure out why and respond
- Example: "Spotify's DAU dropped 25% in the US market. What caused it? How do you respond?"
- Go-to-Market — Launch a product in a new market with a defined budget
- Example: "Launch a new product category with a $500K budget and 6-month timeline"
- Trade-offs — Speed vs. quality, growth vs. monetization, build vs. buy
- Example: "Your CEO demands profitability in 90 days. Growth is at risk. What do you do?"
This tool includes real PM frameworks you'll encounter in interviews and real PM work:
| Framework | Use Case | Scenario Types |
|---|---|---|
| RICE | Prioritization scoring | Prioritization, Trade-offs |
| ICE | Feature ranking | Product Design, Prioritization |
| HEART | Metrics definition | Metrics & Diagnosis |
| North Star | Strategic vision | Product Design, GTM |
| Jobs-to-be-Done | User research | Product Design |
| AARRR (Pirate Metrics) | Growth funnel | Metrics & Diagnosis, GTM |
| MoSCoW | Scope management | Prioritization, Go-to-Market |
- AI Engine: Google Gemini for scenario generation and response evaluation
- Frontend: TypeScript + React for the interface
- Styling: Tailwind CSS for responsive, modern UI
- Deployment Ready: Production-optimized architecture
git clone https://github.com/yatinbhalla/PM-Scenario-Lab.git
cd PM-Scenario-Lab
npm install
echo "GEMINI_API_KEY=your_key_here" > .env.local
npm run devThe app will run on http://localhost:3000
- Practice for PM interviews at FAANG and high-growth startups
- Build a portfolio of well-reasoned case study responses
- Identify and close gaps in your PM thinking before interviews
- Screen PM candidates on structured thinking, not just background
- Identify candidates with strong fundamentals in prioritization, metrics, and strategy
- Use scenario responses as interview discussion starters
- Train aspiring PMs with real-world scenarios and frameworks
- Track mentee progress and identify learning gaps
- Use AI feedback to augment 1-on-1 coaching
Yatin Bhalla · Product Manager & AI Builder
🔗 linkedin.com/in/yatin-bhalla-834632238 · yatinbhalla42@gmail.com