The evolution of artificial intelligence has changed what it means to write software. Coding agents such as Claude, GitHub Copilot, and OpenAI Codex can now generate entire features from a short prompt. Still, the quality of what they produce depends entirely on the quality of the instructions behind it.
This course teaches you how to become an AI Native Engineer: someone who directs AI agents with precision instead of vague requests, and who uses Specification-Driven Development (SDD) across every stage of what is increasingly known as the AI Development Life Cycle, or AI-DLC, to keep that direction consistent, durable, and shareable across a team.
You will learn the theory behind SDD and then apply it through a complete, realistic case study that moves from business objectives to working code: building a backend for a lending platform for a fictional company.
Starting from a business problem and objectives, you will define boundaries, write user-centric scenarios as use cases and user stories, capture non-functional requirements, and model data with entity-relationship diagrams. From there, you will install and use Spec-Kit, the open-source toolkit for SDD, and run its full command workflow across the AI-DLC: constitution, specify, clarify, plan, tasks, analyze, and implement.
