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AI Coding for Senior Engineers

The Reality​

AI coding assistants are production-standard in 2025. Companies ship features faster. Individual engineers 10x their output. The technology works—but most developers hit a frustration wall within weeks.

The problem isn't the tools. It's the operating model.

You're treating AI agents like junior developers: waiting for them to "understand," fixing their code line-by-line, fighting context limits. That's the wrong mental model. AI agents aren't teammates—they're CNC machines for code. You need to learn to operate them.

What This Course Is​

This is operator training for AI coding agents. You'll learn the systematic approach used in production environments:

  • Plan - Break work into agent-appropriate tasks, research architecture, ground in context
  • Execute - Craft precise prompts, delegate to specialized sub-agents, run operations in parallel
  • Validate - Use tests as guardrails, review generated code critically, require evidence of correctness

No hand-holding. No toy examples. This course assumes you know how to engineer software—we're teaching you how to orchestrate agents that execute it autonomously.

What This Course Isn't​

  • Not AI theory - We cover enough internals to operate effectively, nothing more
  • Not prompt templates - Copying prompts doesn't work; understanding principles does
  • Not a replacement for fundamentals - You still need to know architecture, design patterns, and system design
  • Not for beginners - If you don't have production experience, start there first

Who Should Take This​

You're the target audience if you:

  • Have 3+ years professional engineering experience
  • Already tried AI coding assistants and hit frustration points
  • Want to move faster without sacrificing code quality
  • Need to understand codebases, debug issues, or plan features more efficiently
  • Care about production-readiness, not demos

How to Use This Course​

Sequential consumption recommended. Each module builds on previous concepts:

  1. Module 1: Understanding the Tools - Mental models and architecture
  2. Module 2: Methodology - Prompting, grounding, workflow design
  3. Module 3: Practical Techniques - Onboarding, planning, testing, reviewing, debugging

Hands-on exercises are mandatory. Reading alone won't build the operating skills. Work through the exercises on real codebases, not the examples provided.

What You'll Gain​

After completing this course, you'll be able to:

  • Onboard to unfamiliar codebases 5-10x faster using agentic research
  • Refactor complex features reliably with test-driven validation
  • Debug production issues by delegating log/database analysis to agents
  • Review code systematically with AI assistance while maintaining critical judgment
  • Plan and execute features with parallel sub-agent delegation

Most importantly: you'll know when to use agents and when to write code yourself. That judgment is what separates effective operators from frustrated ones.

Prerequisites​

  • Experience: 3+ years professional software engineering
  • Tools: Access to a CLI coding agent (Claude Code, OpenAI Codex, Copilot CLI, etc)
  • Mindset: Willingness to unlearn "AI as teammate" and adopt "AI as tool"

About This Course's Development​

This course practices what it teaches. The entire curriculum—content structure, lesson progression, code examples, and documentation—was developed using the same AI-assisted techniques and workflows you'll learn here.

Every module was planned, researched, drafted, and refined through systematic prompting, agentic research, and iterative validation. The process followed the exact methodology outlined in the course: breaking work into agent-appropriate tasks, grounding in architectural context, and validating output critically.

The podcast version of each lesson was generated using Claude Code and Google's Gemini API—converting technical content into conversational dialogue, then synthesizing multi-speaker audio. The voices you hear (Alex and Sam) are AI-generated, as is their script. Even this acknowledgment exists because we applied the principle of transparency in AI-assisted work.

This isn't marketing. It's validation. If these techniques can produce production-grade training material on their own application, they're robust enough for your codebase.

Ready to start? Begin with Understanding the Tools.