AI Engineering is fundamentally about system building. It is the transition from demos to production-grade Python systems that must be scalable, reliable, and testable. In my experience, one way to achieve this consistently with AI-generated code is to stop coding first — and start specifying first.
Spec-Driven Development is a practical methodology for AI-assisted development. It is not about heavy bureaucracy; it's about creating a "Single Source of Truth" that both humans and AI agents can rely on.
In this talk, I will walk through a realistic feature in a production-grade retrieval-augmented generation system. I will demonstrate how I used SpecKit — one example of a structured spec workflow, usable with different AI coding assistants — to move from a feature request to a reviewable spec, a research document, interface contracts, and a phased task plan — all before writing a single line of implementation code.
What You Will Learn:
