What you'll learn:
- Explain the AI Native Engineer paradigm shift and direct AI coding agents with precise, structured intent instead of vague prompts
- Apply Specification-Driven Development (SDD) across the AI Development Life Cycle (AI-DLC), from business objective to working code
- Install and run GitHub Spec-Kit full command workflow across AI engines such as Claude
- Leverage AI to write user-centric scenarios, translate them into data definitions, specification, and code
- Build custom AI context and skills to turn AI into a specialized business analyst agent
- Generate test scenarios and executable K6 test scripts directly from specifications
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.
Along the way, you will build a custom AI business analyst agent using Claude, connect Claude to external tools through the Model Context Protocol (MCP) with various tools, and generate test scenarios and K6 test scripts straight from your specifications.
Beyond the mechanics, you will get an honest, experience-based look at where Spec-Kit succeeds, where it falls short, and how to review AI-generated specifications and code with healthy skepticism rather than blind trust.
The course covers both a quick path for fast prototyping and a deep path that mirrors a real software development process, so you can choose the pace that fits your goals.
If you want a structured, repeatable way to work with AI coding agents on real projects instead of relying on vibe coding, enroll now and start building your first specification-driven feature today.
