PM Case Study Skill
Generate a detailed PM case study from a real AI product launch, pivot, or strategic decision — reconstructing the PM thinking behind it.
When to Use
- User asks "Write a case study on [AI product launch/decision]"
- User wants to understand PM decisions behind a real product
- User says
/pm-case-study followed by a topic
- Great for: ChatGPT launch, Claude's Constitutional AI, Gemini's multimodal strategy, GitHub Copilot pricing, Perplexity's search bet, Midjourney's Discord-first strategy, etc.
Framework: PM Case Study (8 Sections)
Section 1: Executive Summary
- What happened: One paragraph summary of the product decision/launch
- When: Timeline of key events
- Who: Key people and teams involved
- Outcome: How it played out (success, failure, mixed)
Section 2: Context & Background
- Company situation: Where was the company at this point? Stage, funding, competitive position.
- Market context: What was happening in the broader market?
- Technical context: What capabilities existed? What was newly possible?
- User context: What were users doing before this product? What pain existed?
Section 3: The Decision
- What was decided: Specific product/strategy decision
- Alternatives considered: What other paths were likely on the table?
- Key trade-offs: What did they give up by choosing this path?
- Stakeholder dynamics: Who likely championed this? Who likely opposed it?
Section 4: Execution Analysis
- Go-to-market strategy: How was it launched? Distribution channel?
- Phasing: Was it a big bang launch or phased rollout?
- Pricing: How was it priced? Why that model?
- Technical execution: What was the technical approach? Shortcuts taken?
Section 5: What Went Right
- Identify 3-5 specific decisions that contributed to success
- For each: What was the decision, why it mattered, what would have happened otherwise
- Be specific — reference actual features, timelines, or metrics where available
Section 6: What Went Wrong (or Could Have Been Better)
- Identify 2-3 mistakes, misses, or areas for improvement
- For each: What happened, what the impact was, what could have been done differently
- Be fair — hindsight bias is easy, focus on what was knowable at the time
Section 7: Metrics & Outcomes
- Growth metrics: Users, revenue, market share (use real numbers where available)
- Product metrics: Engagement, retention, satisfaction
- Strategic outcomes: Market position, competitive response, ecosystem effects
- Unexpected outcomes: Things that happened that nobody predicted
Section 8: Key Takeaways
Extract 3-5 lessons for product managers:
- Lesson: Clear statement of the principle
- Application: How to apply this in product sense/strategy decisions
- Example question: A product question where this lesson is directly relevant
Case Study Categories
Product Launches
- ChatGPT's launch (Nov 2022) — fastest growing consumer app ever
- Claude's positioning as the "safe" alternative
- Perplexity's answer engine vs. Google Search
- Midjourney's Discord-native strategy
- Cursor's bet on AI-native IDE
Strategic Pivots
- An AI lab's shift from nonprofit to capped-profit
- A safety lab's pivot from pure research to product company
- A big tech company's emergency response to ChatGPT
- An open-source LLM strategy from a major tech company
Feature Decisions
- ChatGPT Plugins → GPTs → the pivot to actions/agents
- GitHub Copilot's pricing model ($10/month individual)
- Claude's Artifacts feature
- Gemini's multimodal-first approach
- NotebookLM's audio overview feature
Pricing & Business Model
- LLM API pricing evolution (the race to the bottom)
- ChatGPT Plus ($20/month) → Team → Enterprise tiers
- The free tier strategy across AI companies
- Usage-based vs. seat-based pricing in AI
Output Format
Write as a business school case study — structured, analytical, and with clear takeaways. Use real data where available, clearly mark estimates or speculation. Aim for ~2500 words.
Research-First Workflow (CRITICAL)
This skill requires real data:
- Research extensively — Do 10-15 web searches for: launch details, user growth data, pricing history, company blog posts, founder interviews, analyst reports, and competitor responses.
- Cite everything — Include
[linked source](url) inline for all factual claims.
- Date awareness — Note what was known at the time of the decision vs. what we know now.
- Display the complete case study.
What Good Looks Like
- Demonstrates deep knowledge of the AI product landscape
- Shows you can analyze real product decisions with nuance
- Provides concrete examples and data points for product discussions
- Builds pattern recognition across multiple product launches
- Reveals your product judgment when you evaluate decisions
1---2name: pm-case-study3description: Generate end-to-end PM case studies from real AI product launches, pivots, and decisions. Analyzes what happened, why, what the PM likely decided, trade-offs made, and lessons learned.4---56# PM Case Study Skill78Generate a detailed PM case study from a real AI product launch, pivot, or strategic decision — reconstructing the PM thinking behind it.910## When to Use11- User asks "Write a case study on [AI product launch/decision]"12- User wants to understand PM decisions behind a real product13- User says `/pm-case-study` followed by a topic14- Great for: ChatGPT launch, Claude's Constitutional AI, Gemini's multimodal strategy, GitHub Copilot pricing, Perplexity's search bet, Midjourney's Discord-first strategy, etc.1516## Framework: PM Case Study (8 Sections)1718### Section 1: Executive Summary19- **What happened**: One paragraph summary of the product decision/launch20- **When**: Timeline of key events21- **Who**: Key people and teams involved22- **Outcome**: How it played out (success, failure, mixed)2324### Section 2: Context & Background25- **Company situation**: Where was the company at this point? Stage, funding, competitive position.26- **Market context**: What was happening in the broader market?27- **Technical context**: What capabilities existed? What was newly possible?28- **User context**: What were users doing before this product? What pain existed?2930### Section 3: The Decision31- **What was decided**: Specific product/strategy decision32- **Alternatives considered**: What other paths were likely on the table?33- **Key trade-offs**: What did they give up by choosing this path?34- **Stakeholder dynamics**: Who likely championed this? Who likely opposed it?3536### Section 4: Execution Analysis37- **Go-to-market strategy**: How was it launched? Distribution channel?38- **Phasing**: Was it a big bang launch or phased rollout?39- **Pricing**: How was it priced? Why that model?40- **Technical execution**: What was the technical approach? Shortcuts taken?4142### Section 5: What Went Right43- Identify 3-5 specific decisions that contributed to success44- For each: What was the decision, why it mattered, what would have happened otherwise45- Be specific — reference actual features, timelines, or metrics where available4647### Section 6: What Went Wrong (or Could Have Been Better)48- Identify 2-3 mistakes, misses, or areas for improvement49- For each: What happened, what the impact was, what could have been done differently50- Be fair — hindsight bias is easy, focus on what was knowable at the time5152### Section 7: Metrics & Outcomes53- **Growth metrics**: Users, revenue, market share (use real numbers where available)54- **Product metrics**: Engagement, retention, satisfaction55- **Strategic outcomes**: Market position, competitive response, ecosystem effects56- **Unexpected outcomes**: Things that happened that nobody predicted5758### Section 8: Key Takeaways59Extract 3-5 lessons for product managers:60- **Lesson**: Clear statement of the principle61- **Application**: How to apply this in product sense/strategy decisions62- **Example question**: A product question where this lesson is directly relevant6364## Case Study Categories6566### Product Launches67- ChatGPT's launch (Nov 2022) — fastest growing consumer app ever68- Claude's positioning as the "safe" alternative69- Perplexity's answer engine vs. Google Search70- Midjourney's Discord-native strategy71- Cursor's bet on AI-native IDE7273### Strategic Pivots74- An AI lab's shift from nonprofit to capped-profit75- A safety lab's pivot from pure research to product company76- A big tech company's emergency response to ChatGPT77- An open-source LLM strategy from a major tech company7879### Feature Decisions80- ChatGPT Plugins → GPTs → the pivot to actions/agents81- GitHub Copilot's pricing model ($10/month individual)82- Claude's Artifacts feature83- Gemini's multimodal-first approach84- NotebookLM's audio overview feature8586### Pricing & Business Model87- LLM API pricing evolution (the race to the bottom)88- ChatGPT Plus ($20/month) → Team → Enterprise tiers89- The free tier strategy across AI companies90- Usage-based vs. seat-based pricing in AI9192## Output Format93Write as a business school case study — structured, analytical, and with clear takeaways. Use real data where available, clearly mark estimates or speculation. Aim for ~2500 words.9495## Research-First Workflow (CRITICAL)96This skill requires real data:971. **Research extensively** — Do 10-15 web searches for: launch details, user growth data, pricing history, company blog posts, founder interviews, analyst reports, and competitor responses.982. **Cite everything** — Include `[linked source](url)` inline for all factual claims.993. **Date awareness** — Note what was known at the time of the decision vs. what we know now.1004. **Display** the complete case study.101102## What Good Looks Like103- Demonstrates deep knowledge of the AI product landscape104- Shows you can analyze real product decisions with nuance105- Provides concrete examples and data points for product discussions106- Builds pattern recognition across multiple product launches107- Reveals your product judgment when you evaluate decisions
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