Skip to main content
Second Brain
articleJanuary 25, 2026

AI Coding Roadmap: From Zero to Productive

by alexander-opalic
ai-codingclaude-codegithub-copilotdeveloper-experiencelearning

A curated learning path for developers who want to master AI-assisted coding, from basic autocomplete to autonomous agents.

Since March 2025, I've been completely obsessed with AI coding. I've consumed hundreds of articles, tutorials, and podcasts on the topic. This roadmap distills everything I learned into a clear progression from beginner to advanced practitioner.

Level 1: Foundation

Start here. Understand what AI coding actually is before diving into tools.

Understanding LLMs

Before you use AI coding tools, understand how they work:

Core Concepts

  • Building Effective Agents - Anthropic's definitive guide explains the difference between workflows and agents. Read this first to build the right mental model.
  • 12 Factor Agents - HumanLayer's manifesto for production-grade agents. Teaches you that effective AI coding combines deterministic software with strategic LLM decision-making.

Pick Your First Tool

Choose one and stick with it for at least a month:

GitHub Copilot (VS Code)

  • Best for: VS Code users who want inline completions and chat
  • Start with: Using Agents in VS Code to understand the four agent types

Claude Code (Terminal)

Cursor

Level 2: Basic Usage

Master the fundamentals before moving to advanced patterns.

Essential Skills

  1. Write clear prompts - Be specific about what you want. Include context about your codebase.
  2. Use plan mode - Think before coding. Let the AI propose an approach before implementation.
  3. Review every change - Never blindly accept AI suggestions. You own the code.

Claude Code Basics

  • Learn /init to generate your first CLAUDE.md file
  • Master ! prefix for instant bash execution
  • Use --continue to resume sessions
  • Practice Escape to rewind mistakes

VS Code Copilot Basics

Level 3: Context Engineering

This is where most developers plateau. Context engineering separates productive AI coders from frustrated ones.

The Core Principle

AI models have limited attention. Every token counts. Feed them exactly what they need—nothing more, nothing less.

Key Resources

Configuration Files

Every AI coding tool reads project-specific instructions. Master these:

Key Insight

Don't dump your entire codebase into context. Use progressive disclosure—start minimal and add context only when needed.

Level 4: Skills and Extensions

Extend your AI coding tool with reusable workflows.

Claude Code Skills

VS Code Agent Skills

Safety

Before going autonomous, learn to protect yourself:

Level 5: Advanced Patterns

Now you're ready for multi-agent orchestration and autonomous loops.

The Ralph Technique

Ralph solves context window limitations by breaking work into independent iterations. Each session gets fresh context, implements one story, and commits before the next begins.

Agent Architecture

Multi-Agent Workflows

Level 6: Production Mindset

Think about quality, testing, and sustainability.

Test-Driven Development

AI hallucinations are real. TDD catches them early.

Quality Patterns

Workflow Integration

Books

These books shaped my understanding of AI coding. Read them in order.

Foundations

Patterns and Practice

Podcasts and Talks

Recommended Learning Order

  1. Week 1-2: Watch Karpathy's Deep Dive into LLMs. Read Building Effective Agents and 12 Factor Agents. Pick your tool.
  2. Week 3-4: Master basic usage. Create your first CLAUDE.md or copilot-instructions.md.
  3. Month 2: Deep dive into context engineering. Read all three resources in Level 3. Start AI Engineering by Chip Huyen.
  4. Month 3: Build your first custom skill. Experiment with subagents. Read Agentic Design Patterns.
  5. Month 4+: Try the Ralph technique on a real project. Iterate on your workflow.

Curated Collections

For ongoing reference, bookmark these:

Industry Perspectives

Understand where this is heading:

Final Advice

Start simple. The most effective AI coding setups are often the simplest. Add complexity only when you hit real limitations.

The goal isn't to automate yourself out of thinking—it's to amplify your thinking. You remain the architect. The AI executes.

Related Reading