Getting code from an AI assistant is easy. Getting code that works, fits your project, and handles edge cases correctly is a different skill entirely — and it starts with how you write the prompt. This module teaches you to construct precise, context-rich prompts that specify tech stack, desired behavior, output format, and constraints, so that AI tools produce reliable code rather than plausible-looking fragments. You will practice iterative refinement techniques, learn how to structure multi-step prompts across extended sessions, and diagnose the most common failure modes in AI-generated code. By the end of this module, you will be able to write prompts that consistently produce working, well-structured code and systematically improve results when the output falls short.

This module simplifies the core developer tools needed to build, manage, and version-control web applications, focusing on beginner-friendly setups and AI-assisted workflows. You will learn to set up a local development environment, use code editors like VS Code and Cursor, run basic terminal commands, and manage project files with Git. Through guided tutorials and hands-on practice, you will gain confidence using the terminal, understand package managers such as npm and pip, and explore how AI assistants integrate into modern IDEs. The module also covers essential Git workflows, including repositories, commits, branching, and GitHub collaboration, to keep projects organized, versioned, and shareable.

This module guides you through building a simple, functional web application using AI coding assistants, from concept to working prototype. Applying skills from previous modules, you will create beginner-friendly projects such as a portfolio site, to-do app, or landing page. Through step-by-step tutorials, you will scaffold project files, generate HTML, CSS, and JavaScript with AI, add basic interactivity, and organize your codebase for maintainability. The module emphasizes iterative development, encouraging you to build an MVP, test frequently, and refine using AI feedback and user testing. By the end, you will have completed a working AI-assisted web application and strengthened your vibe coding workflow skills.

This module teaches you how to deploy a locally built web application to a live URL, making it accessible online. You will explore beginner-friendly platforms such as Vercel, Netlify, and Railway, and learn to connect GitHub repositories, configure environment variables, and automate deployments through CI/CD workflows. Through guided tutorials, you will deploy static frontend applications, understand static versus full-stack hosting, and troubleshoot common deployment issues. The module also covers domain setup, custom URLs, and basic performance optimization to improve reliability and speed. By the end, you will confidently deploy web applications, manage production settings, and independently ship future projects online.

This capstone lab gives you the opportunity to apply everything you have learned across Course 1 in one integrated, hands-on exercise. Working through a realistic client scenario, you will write and refine an AI prompt, read and debug AI-generated Python code, decompose a feature request into buildable components, and identify what needs to happen before an app reaches real users. No prior programming experience is required. By the end, you will have practiced the complete vibe-coding workflow — from idea to working code.