LearnQuest

Scale and Professionalize AI-Built Projects

LearnQuest

Scale and Professionalize AI-Built Projects

LearnQuest Network

Instructor: LearnQuest Network

Included with Coursera PlusLearn more

Gain insight into a topic and learn the fundamentals.
Beginner level

Recommended experience

3 hours to complete
Flexible schedule
Learn at your own pace
Gain insight into a topic and learn the fundamentals.
Beginner level

Recommended experience

3 hours to complete
Flexible schedule
Learn at your own pace

What you'll learn

  • Refactor AI-generated code and manage technical debt before it slows you down

  • Add features to growing multi-feature apps without breaking what already works

  • Debug systematically and document projects so teams and reviewers trust them

Details to know

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Recently updated!

September 2026

Assessments

5 assignments

Taught in English

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There are 5 modules in this course

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.

What's included

8 videos1 reading1 assignment

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.

What's included

7 videos1 assignment

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.

What's included

7 videos1 assignment

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.

What's included

7 videos1 assignment

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.

What's included

8 videos1 assignment

Instructor

LearnQuest Network
LearnQuest
228 Courses1,029,279 learners

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LearnQuest

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