AI coding demos look impressive. But they rarely survive contact with real software teams.
Most AI tools promise to 10x your development speed. Then reality hits: unclear ownership, harder code reviews, eroding architecture, and teams that slow down instead of speeding up.
This book shows you a better way.
What You'll Learn:
✅ Why AI fails in real teams (and how to fix it with spec-driven development)
✅ The critical distinction between Architect Specs and Development Specs
✅ How to use AI as a planner, not a decision-maker (the most important control point)
✅ Execution with history - making AI a reliable long-term teammate
✅ How to reduce review costs instead of increasing them
✅ A proven workflow that preserves ownership, reduces risk, and builds trust
✅ Team adoption strategies that work without disrupting your SDLC
This Book Is For:
- Software engineers working in team environments
- Tech leads and architects who need to introduce AI safely
- Engineering managers concerned about code quality and ownership
- Anyone responsible for maintaining systems over time
What Makes This Different:
Unlike prompt collections or tool comparisons, this book focuses on structure and process. You'll learn how to integrate AI into your existing SDLC without breaking the principles that allow teams to build systems that last.
No vendor lock-in. No specific tools required. Just principles that work.
