EduGen spent eighteen months and twelve million dollars building an AI-powered course generation platform. They had impressive demos, strong investor backing, and a team of talented AI engineers. They launched to crickets. Users tried the product once, encountered hallucinations in the generated content, and never came back. The team had not built eval infrastructure, had not tested with real learners, and had not understood that AI-generated educational content requires human verification that takes more time than writing the content from scratch. This is the failure case study. The others succeed, but not without struggle. Real products. Real decisions. Real outcomes. These case studies show how companies have navigated the AI product development journey from start to finish, including the graveyard they had to walk through to get there.
The Tripartite Loop in Case Studies
Case studies reveal how all three disciplines work in practice: AI PM perspectives show how product decisions were made; Vibe-Coding perspectives show how rapid iteration was achieved; AI Engineering perspectives show how production challenges were solved.
Objective: Learn from real-world AI product experiences across multiple domains and understand what separates success from failure.
Chapter Overview
This chapter presents five detailed case studies that illustrate the complete lifecycle of AI products. Each case study covers the problem space, decisions made, trade-offs considered, and outcomes achieved. One case study focuses on failure and the lessons learned.
Four Questions This Chapter Answers
- What are we trying to learn? How real companies have navigated AI product development decisions, trade-offs, and outcomes across multiple domains.
- What is the fastest prototype that could teach it? Deep reading of one case study relevant to your product context and mapping its decisions to your own situation.
- What would count as success or failure? Patterns that repeat across successful AI products and anti-patterns that consistently lead to failure.
- What engineering consequence follows from the result? Case studies provide pattern recognition that accelerates AI product development by learning from others' experiences.
Learning Objectives
- Analyze real AI product decisions and their consequences
- Understand how different AI product categories approach common challenges
- Apply lessons from both successes and failures to your own products
- Recognize patterns that repeat across AI product development