About Vibe Coding
Vibe coding is building software by describing what you want in plain language and
letting an AI assistant write the code. You steer with prompts, run
the result, react to what you see, and keep refining until the app does what you had in
mind. It has opened software creation to people who have never written code, and it has
given experienced developers a much faster way to prototype.
The course starts with the Foundations: what vibe coding is, where it
works well and where it doesn't, and how to set up the AI coding tools
you'll use. You then learn the core skill, prompting for code, along
with how to plan a project, write a short spec, and give the AI the right context.
Building and iterating is where it comes together: creating your first
app, steering it through changes, and debugging with AI when things
break. Alongside that you learn to read and review the code the AI
produces, because understanding what you ship is what separates a demo from a product.
From there you go further: agentic coding workflows where the AI plans
and edits across a whole project, adding databases, logins and APIs,
using Git and deploying to the web, and covering the
security, testing and quality checks that AI-written code needs before
real users touch it.
Tools in this space change almost monthly, so the course teaches habits that last: how
to break problems down, how to describe them clearly, and how to verify the result. Each
module builds on the one before it, and you finish with projects you can show.
Content
12 modules
01
Introduction to Vibe Coding
What vibe coding is, how it differs from traditional programming, what it's good for, and the mindset that gets the best results from an AI collaborator.
What Is Vibe Coding
Use Cases
Strengths & Limits
Mindset
02
AI Coding Tools & Setup
A tour of the main kinds of AI coding tools: chat assistants, AI code editors, prompt-to-app builders, and terminal agents, and how to pick and set one up.
AI Editors
App Builders
Terminal Agents
Setup
03
Prompting for Code
Writing prompts that produce working software: being specific, giving examples and constraints, asking for one change at a time, and recovering when the AI goes off track.
Clear Prompts
Constraints
Examples
Small Steps
04
Planning, Specs & Context
Deciding what to build before you prompt: writing a short product spec, breaking it into features, and giving the AI the context and rules files it needs to stay consistent.
Product Spec
Feature Breakdown
Context
Rules Files
05
Building Your First App
Going from an idea to a running app: scaffolding a project, generating the interface, adding features step by step, and seeing changes live in the browser.
Scaffolding
UI Generation
Live Preview
Feature by Feature
06
Iterating & Debugging with AI
Fixing what breaks: reading error messages, giving the AI useful bug reports, using the browser console and logs, and knowing when to undo and try a different approach.
Error Messages
Bug Reports
Console & Logs
Rollbacks
07
Reading & Reviewing AI Code
Understanding what the AI wrote: project structure, core web concepts, spotting duplicated or fragile code, and asking the AI to explain and simplify its own output.
Project Structure
HTML / CSS / JS
Code Review
Refactoring
08
Agentic Coding Workflows
Letting an AI agent plan and carry out multi-step changes across a whole codebase: setting goals, reviewing diffs, using tools, and keeping the agent on a short leash.
AI Agents
Multi-file Edits
Reviewing Diffs
Tool Use
09
Databases, Auth & APIs
Making apps that remember things and talk to the world: storing data, adding user accounts, connecting third-party APIs, and handling keys and secrets safely.
Databases
Authentication
APIs
Secrets
10
Version Control & Deployment
Saving your work and putting it online: Git and GitHub basics, committing often so you can undo AI mistakes, and deploying to a live URL with a custom domain.
Git
GitHub
Hosting
Domains
11
Security, Testing & Quality
Making AI-written code safe and reliable: common security mistakes, protecting user data, writing tests with AI, and checking performance and accessibility before launch.
Security
Testing
Data Privacy
Quality Checks
12
Limits & Shipping Real Products
Knowing where vibe coding breaks down, when to bring in an engineer, how to maintain what you've built, and how to launch, get feedback, and keep improving.
Limitations
Maintenance
Launching
Feedback
What You Can Do After This Course
By the end of the curriculum you'll have built and launched working projects with AI
and learned the habits that keep AI-written software reliable. Concretely, you'll be
able to:
Turn ideas into working apps — describe what you want and steer an AI assistant from a blank screen to a running product.
Prompt with precision — write clear, specific instructions and recover quickly when the AI misunderstands.
Plan before you build — write short specs and give the AI the context it needs to stay consistent across a project.
Debug and understand your code — read errors, review what the AI wrote, and fix or simplify it instead of treating it as a black box.
Work with AI agents — run multi-step changes across a codebase while reviewing every diff before it lands.
Add real features — connect databases, user accounts and APIs, and handle keys and user data responsibly.
Ship with confidence — use Git, deploy to a live URL, check security and quality, and know when to call in an engineer.