Overview

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This course teaches you to take AI-assisted projects from working prototypes to production-ready, team-ready work. You'll learn to recognize and fix structural debt in AI-generated code, add features to growing applications without breaking existing functionality, replace guesswork debugging with systematic testing and observability, prepare contributions that meet the standards of a shared codebase, and document and present your projects so they earn trust with recruiters, clients, and collaborators.

Syllabus

Maintaining Code Quality in AI-Assisted Projects
- Your AI-generated codebase works today, but every day it goes unmanaged, it gets more expensive to change. This module teaches you to recognize the code smells, technical debt, and architectural drift that build up as speed outpaces structure, and to translate those signals across engineering, product, and junior-developer perspectives. You'll learn the four practices that keep AI-generated code changeable: modular file architecture, intention-revealing naming, targeted inline comments, and a safe, test-backed refactoring process. You'll also see how AI tooling is shifting toward project-aware refactoring assistance — and why managing debt continuously still depends on you, not the model.
Managing Complexity in Multi-Feature Applications
- As your application grows from one feature to a dozen, changes in one place start rippling unpredictably into others. This module addresses that inflection point directly. You'll learn to decompose a user interface into component-based architecture, categorize state as local, shared, or global so you stop fighting your own data, organize backend logic into route, service, and data-access layers, and apply a version-control discipline that keeps parallel work from colliding. You'll also work through the perspectives of frontend developers, backend developers, tech leads, and product managers, who each describe the same complexity in different language.
Advanced Debugging and Testing Workflows
- Speed becomes a liability the moment a user reports a failure you can't reproduce. This module moves you from ad hoc debugging toward systematic testing and evidence-based diagnosis. You'll learn to think in test layers — unit, integration, and end-to-end — and use automated testing as a continuous feedback mechanism rather than a final checkpoint. You'll build a hypothesis-driven debugging method grounded in reproducible evidence, and use observability — structured logs, error rates, and traces — to see what a running system is actually doing. You'll also see how AI is moving from passive stack-trace lookup toward active involvement across the incident lifecycle.
Collaborating with Technical Teams Using AI Tools
- Working alone with AI is forgiving; joining a team is not. This module prepares you for the transition from solo builder to team contributor. You'll learn what structured code review actually looks for, how to write a bug report a developer can act on without follow-up questions, how GitHub Flow moves code from idea to deployment through reviewable steps, and how documentation functions as a team's shared memory. You'll also see how AI is beginning to participate in review and triage itself — and why the standards for a reviewable pull request or an actionable bug report don't change because of it.
Documenting and Presenting AI-Built Projects Professionally
- Your project can be genuinely good and still be invisible if it can't answer the questions every reviewer brings to it: what does this do, who is it for, can I run it, what did you actually build. This module moves you from builder to presenter. You'll build a README that functions as a contract with any reader, script a demo with a real narrative arc, frame a project as a portfolio case study rather than a technology list, and develop layered technical storytelling for audiences with different backgrounds. You'll also learn to use AI to draft these artifacts without losing the credibility that comes from verified facts and a tone you can defend.