| name | ai-product-teardown |
| description | Structured teardown of AI products (ChatGPT, Claude, Gemini, Perplexity, Copilot, etc.). Analyzes product decisions, UX patterns, technical architecture, business model, and competitive positioning. |
| argument-hint | [product name or feature] |
AI Product Teardown Skill
Perform a structured, opinionated teardown of any AI product — analyzing the product decisions, UX, technical architecture, business model, and competitive positioning from a PM lens.
When to Use
- User asks "Tear down [AI product]" or "Analyze [AI product]"
- User wants to understand the product thinking behind an AI feature
- User wants to build product intuition about AI products
- User says
/ai-product-teardown followed by a product name
- Great for: ChatGPT, Claude, Gemini, Perplexity, Copilot, Midjourney, Cursor, v0, NotebookLM, etc.
Framework: AI Product Teardown (7 Sections)
Section 1: Product Overview
- What it is: One-sentence description
- Company: Who built it, their mission, and strategic context
- Launch date & trajectory: When launched, key milestones, current scale
- Target users: Primary and secondary audiences
- Business model: How it makes money (or plans to)
Section 2: Core Value Proposition
- Job to be Done: What fundamental job does this product do for users?
- 10x moment: What's the moment where users think "this is magic"?
- Switching cost: What would it take to switch away?
- Network effects: Does it get better with more users? How?
Section 3: UX & Product Decisions
Walk through the key product decisions and evaluate each:
- Onboarding flow: How does a new user go from zero to value?
- Core interaction model: Chat? Canvas? Structured output? Multi-modal?
- Information architecture: How is functionality organized?
- Personalization: How does it adapt to different users?
- Error handling: What happens when the AI is wrong?
For each decision, evaluate:
- What they got RIGHT and why
- What they got WRONG or could improve
- What trade-off they're making (and whether you'd make the same one)
Section 4: Technical Architecture (PM Lens)
Analyze the technical choices from a product perspective:
- Model strategy: Which model(s)? Why that capability level?
- Latency vs. quality trade-off: Where do they sit on the spectrum?
- Context & memory: How does it handle conversation history?
- Safety & guardrails: What's their content policy approach?
- Tool use / plugins / integrations: How extensible is it?
- Pricing architecture: How do technical costs map to pricing?
Section 5: Growth & Distribution
- Acquisition channels: How do users find this? (organic, viral, paid, partnerships)
- Activation: What gets users to the "aha moment"?
- Retention loops: What brings users back?
- Monetization: Free → paid conversion strategy
- Viral mechanics: Does usage naturally create awareness?
Section 6: Competitive Positioning
- Direct competitors: Who else does this job?
- Positioning map: Plot on 2x2 (e.g., capability vs. safety, consumer vs. enterprise)
- Sustainable moats: What's defensible? (data, distribution, brand, model quality, ecosystem)
- Vulnerability: Where could a competitor win?
Section 7: PM Recommendations
If you were the PM, what would you do next?
- Top 3 features to build (with reasoning and expected impact)
- Top 1 thing to kill or change (what's not working)
- Strategic bet: One big swing that could transform the product
- Metrics to watch: What would you track weekly?
Output Format
Write as an opinionated product review — structured but with a clear point of view. Use screenshots/descriptions of specific UI elements where relevant. Aim for ~2000 words. Be specific and cite real features.
Research-First Workflow
- Research — Search for latest product updates, user reviews, competitor announcements, company blog posts, and usage data. Do 5-10 searches.
- Cite sources — Include
[linked source](url) inline for factual claims.
- Display the complete teardown.
What Good Looks Like
- Shows you've done homework on the product landscape
- Demonstrates structured product thinking on real products
- Reveals your product taste and judgment
- Provides concrete examples to reference in product discussions
- Builds intuition about AI product patterns across the industry
