/ai-product-teardown

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

1. **Research** — Search for latest product updates, user reviews, competitor announcements, company blog posts, and usage data. Do 5-10 searches. 2. **Cite sources** — Include `[linked source](url)` inline for factual claims. 3. **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