AI Coding Tools Masterclass
AI coding tools are not just autocomplete on steroids — used well, they change how you architect, review, test, and debug. This course teaches the mental models, tool-specific techniques, and workflow patterns that separate developers who leverage AI effectively from those who fight it.
What you'll learn
Course outline
Free — no account needed
The AI Coding Landscape — Tools, Models, and Mental Models
Autocomplete vs agentic, tool comparisons, model differences, and what "context" means for AI coding tools
Cursor Deep Dive — Composer, Agent Mode, and Rules
Project setup, .cursorrules, Composer for multi-file edits, Agent mode, @ references, best prompting patterns
Claude Code — Terminal-First Agentic Coding
Installation, MCP servers, /commands, hooks, working on large codebases, when to use Claude Code vs in-editor tools
Full course — $49 one-time
Effective Prompting for Code
Asking for implementations, reviews, refactors, debugging, tests — and the right level of specificity
Context Management at Scale
Understanding context windows, reading vs editing, when to start a new conversation, CLAUDE.md files, project-level instructions
AI-Assisted Code Review and Testing
Using AI to review your own code, generating test suites, catching security issues, PR descriptions, where AI review falls short
Building a Workflow — AI Tool Chains and Debugging AI-Written Code
Combining tools, Claude Code for architecture, Cursor for implementation, AI for review, debugging AI output, when to reject it
Get the full course
7 lessons — practical, project-based, no fluff.
About this course
AI coding tools have changed what it means to be a developer. GitHub Copilot, Cursor, and Claude Code are not just autocomplete — they can write full functions, refactor entire files, generate tests, explain unfamiliar code, and act as a pair programmer who never gets tired. The developers who treat these tools as a distraction miss the 10–40% productivity gains they deliver on the right tasks. This course teaches you how to use the current generation of AI coding tools effectively, including when to trust them, when to verify, and how to build a workflow around them.
The main skill gap is not learning which tool to install — it is learning how to prompt, how to verify AI-generated code, and how to integrate AI assistance into a development workflow without creating technical debt. This course covers prompting patterns, context management, code review of AI output, and the task categories where AI tools genuinely accelerate work versus where they slow you down.
Frequently asked questions
What is the difference between GitHub Copilot, Cursor, and Claude Code?
GitHub Copilot is an IDE extension (VS Code, JetBrains) that suggests code inline as you type, trained on public GitHub repositories. Cursor is an IDE (VS Code fork) with deeper AI integration — multi-file context, agent mode for autonomous edits, and a chat sidebar. Claude Code is a terminal-based AI agent from Anthropic that can read your codebase, write files, run commands, and complete multi-step tasks. They serve different use cases: Copilot for inline suggestions, Cursor for IDE-native AI edits, Claude Code for agentic tasks.
How do I avoid shipping AI-generated bugs?
Treat AI-generated code as a first draft from a smart junior developer who sometimes hallucinates APIs. Review every suggestion before accepting it. Specifically: check that referenced functions and APIs actually exist, run the code rather than just reading it, write tests for generated functions, and be skeptical of any generated code that touches auth, payments, or data mutation. The pattern that works best: use AI for the first draft, then review, test, and refine.
What tasks are AI coding tools actually good at?
High-value tasks: boilerplate and scaffolding (CRUD routes, component templates), translating between languages or frameworks, writing tests for functions you have already written, explaining unfamiliar code, generating type definitions from JSON or API responses, writing regex patterns, and SQL query generation. Lower-value tasks: complex business logic where the AI cannot understand your domain constraints, security-critical code where subtle mistakes are dangerous, and novel algorithms where the AI is likely to produce plausible-looking incorrect code.
Does using AI coding tools mean I do not need to learn to code properly?
No — the opposite. Developers who cannot read and reason about code cannot verify AI output, cannot debug AI mistakes, and cannot direct AI tools effectively. The productivity gains from AI tools compound with coding skill — a senior developer with good AI prompting patterns produces dramatically more than a beginner using the same tools. AI tools are a force multiplier, not a replacement for understanding.
What is Cursor agent mode and how is it different from regular AI assistance?
Cursor agent mode allows the AI to autonomously execute a multi-step task: read multiple files for context, write code changes across files, run terminal commands, read the output, and iterate until the task is complete. It is closer to a fully autonomous coding agent than a suggestion system. Agent mode is powerful for scaffolding new features, refactoring across a codebase, and multi-step migrations — but requires careful review since it can make many changes before stopping.