AI coding tools like GitHub Copilot are transforming how software gets built. Instead of writing every line manually, modern developers guide, review, and supervise AI-generated output. This course teaches you to thrive in that new role — known as supervisory engineering — by building a functional web application with AI assistance from start to finish.
You'll begin by configuring GitHub Copilot in VS Code and learning its three core interfaces: Inline Suggestions, Chat View, and Copilot Edits. From there, you'll master the GCSE (Goal, Context, Source, Expectations) prompting framework, a structured approach to writing precision prompts that reduce ambiguity and minimize AI hallucination.
As your application grows, you'll curate richer context using variables like #file and #codebase alongside slash commands such as /fix and /tests. You'll create Instruction Files to enforce coding standards project-wide and manage the Working Set to orchestrate coordinated multi-file refactors in a single pass.
In the final module, you'll delegate complex tasks to Agent Mode with structured oversight strategies and apply iterative debugging feedback loops to diagnose and resolve errors efficiently. Each module includes case studies, hands-on exercises, and graded assessments that reinforce real-world application.
By course end, you'll have a working application, documented prompts, and a reusable supervision workflow for your portfolio. Ideal for developers with basic programming knowledge and VS Code familiarity who want to integrate AI coding tools into their daily practice.
