AI-Native Development
For working software engineers
Operate as an AI-native engineer where AI is core infrastructure, not an add-on. 6-8 hours of hands-on training with workbook exercises and code sandboxes.
This is a premium course
The Developer track requires a paid enrollment. Contact us or upgrade your account to unlock access.
Modules
The Paradigm Shift
What AI-native means and why it matters now. Includes self-assessment, five core principles, and 3.5 hours of workbook exercises.
The Tools Landscape
How LLMs actually work, the four tool categories, the API layer, and how to choose tools and models defensibly.
Prompt Engineering for Engineers
Treat every prompt as a specification — five components, eight battle-tested patterns, context engineering, evals, and four hands-on Cursor exercises along the way.
AI-Native Workflows
A concrete operational playbook: requirements, design, implementation, two-pass review, testing, debugging, docs, and CI/CD.
Architecture and Design
Design systems with AI as a first-class component: RAG architecture, MCP servers, eval infrastructure, cost design, and human-in-the-loop boundaries.
The AI-Native Team
Scale from individual practice to team culture: adoption curve, standardization, governance, training, measurement, continuous improvement.
Capstone Project
Prove the shift. Build something real, end-to-end, AI-native, with a project journal and metrics comparison.