AI Web Development Course

Lesson 1 • Core Web Technologies Refresher

Reviews HTML, CSS, and JavaScript fundamentals as the substrate AI tools operate on. Ensures all students share a common technical baseline before AI-specific work begins.

Lesson 2 • The AI-Augmented Developer Mindset

Defines AI's practical role as a coding collaborator, not a replacement. Sets expectations for human oversight and prompt-driven development throughout the course.

Lesson 3 • Overview of AI Coding Tools

Surveys the landscape of AI code assistants, chat-based tools, and generative IDEs. Establishes criteria for selecting the right tool per task type.

Lesson 1 • Prompt Chaining and Iterative Refinement

Introduces multi-step prompt sequences that build complex features incrementally. Develops the habit of reviewing and redirecting AI output between each chain step.

Lesson 2 • Debugging and Fixing AI-Generated Code

Covers strategies for identifying errors in AI output and prompting targeted fixes. Builds critical evaluation skills essential for safe use of generated code.

Lesson 3 • Prompting for JavaScript Logic

Teaches prompt strategies for generating functions, event handlers, and async logic. Connects prompt precision to code correctness and maintainability.

Lesson 4 • Anatomy of an Effective Code Prompt

Breaks down the components of a high-quality prompt: context, constraints, format, and examples. Directly improves output quality for all AI coding tasks in this course.

Lesson 1 • Generating Navigation and Layout Components

Applies AI prompting to build headers, navbars, footers, and grid layouts. Covers responsive behavior and cross-browser compatibility in generated output.

Lesson 2 • Forms, Inputs, and Validation Components

Generates accessible form components with client-side validation using AI assistance. Connects form UX patterns to real-world data collection requirements.

Lesson 3 • Interactive UI Elements and Animations

Creates modals, accordions, tabs, and CSS animations through AI-driven generation. Emphasizes performance-conscious animation and keyboard accessibility.

Lesson 4 • Component-Driven Development Principles

Establishes the component model as the structural foundation for AI-assisted UI work. Explains how AI tools map to component boundaries and reuse patterns.

Lesson 1 • API Integration and Data Fetching

Prompts AI to generate fetch, async/await, and error-handling patterns for REST APIs. Covers loading states, caching hints, and data normalization in the UI layer.

Lesson 2 • Routing and Navigation Logic

Generates client-side routing configurations and protected route patterns using AI. Covers dynamic routes, query parameters, and navigation guards.

Lesson 3 • Choosing and Scaffolding a JS Framework

Compares major JavaScript frameworks and uses AI to scaffold project structures. Establishes the project foundation for all framework-based work in this chapter.

Lesson 4 • Generating Framework Components with AI

Applies prompting to generate framework-specific components with props, slots, and lifecycle hooks. Covers adapting generic AI output to framework conventions.

Lesson 1 • Error Handling and API Security

Prompts AI to implement centralized error handling, input sanitization, and rate limiting. Connects back-end security practices to production readiness standards.

Lesson 2 • Authentication and Authorization Systems

Generates token-based authentication flows and role-based access control using AI. Emphasizes security review of all AI-produced auth code before deployment.

Lesson 3 • Designing and Building REST APIs

Uses AI to design RESTful endpoint schemas and generate route handlers. Covers request validation, response formatting, and HTTP status code conventions.

Lesson 4 • Database Modeling and Queries with AI

Applies AI to generate data models, schema definitions, and optimized queries. Covers relational and document-based database patterns side by side.

Lesson 1 • Testing Full-Stack Integrations

Uses AI to generate unit, integration, and end-to-end test suites for the full-stack app. Covers test coverage analysis and AI-assisted test case generation.

Lesson 2 • Performance Optimization Across the Stack

Applies AI to identify and resolve bottlenecks in rendering, API response time, and database queries. Produces measurable performance improvements using profiling data.

Lesson 3 • Authentication Flow Integration

Wires front-end auth UI to back-end token endpoints using AI-generated integration code. Covers token storage, refresh logic, and protected route enforcement.

Lesson 4 • Real-Time Features with AI Assistance

Introduces WebSocket and server-sent event patterns generated with AI support. Applies real-time data flow to notifications, live updates, and collaborative features.

Lesson 1 • Monitoring, Logging, and Alerting Setup

Uses AI to configure application monitoring, structured logging, and alert thresholds. Connects observability setup to proactive incident response in production.

Lesson 2 • Cloud Infrastructure as Code

Generates infrastructure-as-code templates for cloud resource provisioning using AI. Covers compute, storage, networking, and managed service configuration.

Lesson 3 • Environment Management and Secrets

Applies AI to generate environment configuration strategies and secrets management patterns. Prevents credential exposure across development, staging, and production environments.

Lesson 4 • CI/CD Pipeline Automation

Uses AI to scaffold continuous integration and delivery pipeline configurations. Covers automated testing, build triggers, and deployment stage definitions.

Lesson 1 • Architectural Decision-Making with AI

Leverages AI to evaluate architectural trade-offs, generate ADRs, and model system diagrams. Develops judgment for when to accept, modify, or reject AI architectural suggestions.

Lesson 2 • Scalability and Maintainability Planning

Uses AI to project scaling requirements, generate load-testing scripts, and document maintenance plans. Prepares the application for growth beyond initial launch.

Lesson 3 • Security Hardening with AI Assistance

Applies AI to scan for common vulnerabilities, generate security patches, and produce threat models. Reinforces the principle that AI output requires human security validation.

Lesson 4 • Capstone Project and Portfolio Delivery

Integrates all course skills into a complete, deployed full-stack project with AI-assisted documentation. Produces a portfolio-ready artifact demonstrating end-to-end AI-augmented development.