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.

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.

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.

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.