Build Real-World Applications with AI

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Overview

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This course advances your AI-assisted development skills by guiding you through the process of building real-world, user-facing web applications that integrate dynamic data, backend logic, and external services. You will learn to decompose product ideas into prioritized, AI-executable tasks, debug and iterate on AI-generated code systematically, and deploy full-stack applications that connect frontend interfaces to backend APIs and databases. Through hands-on projects, you will integrate third-party services — including authentication (Supabase, Auth0), payments (Stripe), and AI APIs (OpenAI) — and develop the ability to read API documentation, generate integration code using AI, and handle errors gracefully. This course emphasizes practical workflows commonly faced in the first 9 months of a software development role, preparing you to prototype and ship functional applications independently, collaborate effectively with technical teams, and respond to real-world user needs. By the end of this course, you will have built and deployed a portfolio-ready full-stack application, mastered debugging and iteration workflows for AI-generated code, and gained confidence integrating external APIs to extend application functionality.

Syllabus

Decomposing Product Ideas into Buildable Tasks
- This module teaches you how to break down high-level product ideas into discrete, prioritized, and AI-executable development tasks. You will learn to translate vague feature requests into structured user stories, UI component lists, data models, and API endpoint specifications that can be handed off to AI coding assistants iteratively. Through case studies and hands-on exercises, you will explore frameworks for product decomposition — including user story mapping, wireframing, and task prioritization (MoSCoW, RICE) — and apply these techniques to your own project ideas. This module emphasizes the importance of starting with a minimal viable product (MVP), identifying core functionality, and deferring non-essential features to later iterations. By the end of this module, you will be able to decompose a product idea into a prioritized backlog of AI-executable tasks, create clear specifications for each task, and iteratively build features in a logical, testable sequence.
Debugging and Iterating on AI-Generated Code
- This module gives you a systematic workflow for diagnosing and fixing AI-generated code when it breaks or behaves unexpectedly. You will learn to read error messages and stack traces, use browser developer tools to inspect application state and network requests, and apply iterative prompt refinement to guide your AI assistant toward reliable fixes. You will also practice identifying the failure modes most common in AI-generated code — hallucinated functions, deprecated APIs, missing error handling — and build basic testing habits to catch regressions before they reach users. By the end of this module, you will be able to debug AI-generated code methodically, rather than guessing, and iterate your way to a stable, production-ready result.
Building and Deploying Full-Stack Applications
- This module takes you from isolated frontend prototypes and local scripts to a cohesive, deployed full-stack application that real users can access. You will build a backend API using beginner-friendly frameworks, connect it to a database, implement authentication, and wire your frontend to the backend across a live deployment. Along the way, you will handle the production concerns that catch most beginners off guard: environment variables, cross-origin request configuration, and secure secret management. By the end of this module, you will have built and deployed a full-stack application with working data persistence, user authentication, and a frontend and backend communicating correctly in production.
Integrating Third-Party APIs and Services
- This module teaches you how to extend your applications by integrating the external services that define most real products: payment processing, real-time databases, and AI capabilities. You will learn to read API documentation beyond the quickstart, implement authentication correctly using API keys and token-based flows, handle webhooks for event-driven billing and data sync, and build resilience into every integration through structured error handling, retry logic, and rate-limit awareness. You will work hands-on with Stripe, Supabase, and the OpenAI API. By the end of this module, you will be able to connect external services into a working application securely, handle failures gracefully, and debug integration errors systematically.
Shipping and Iterating on User-Facing Projects
- This module covers what happens after your application is live: running structured beta tests, collecting and centralizing user feedback, instrumenting analytics to track real behavior, monitoring for errors and downtime, and using prioritization frameworks to decide what to fix or build next. These are not optional finishing steps — they are the practices that turn a one-time deployment into a product that improves over time. You will work with the tools and workflows that small, AI-assisted teams rely on to stay close to their users. By the end of this module, you will be able to run a repeatable ship-and-iterate cycle grounded in real data rather than guesswork.