Matt Pocock – AI Coding for Real Engineers (June 1 – June 12, 2026)

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Matt Pocock – AI Coding for Real Engineers (June 1 – June 12, 2026)

The rapid rise of tools like Claude Code, Codex, and other AI coding assistants has fundamentally changed software development.

Many developers now generate code faster than ever before, but speed alone does not guarantee quality.

Poorly guided AI can introduce technical debt, architectural inconsistencies, hidden bugs, security risks, and maintainability problems.

AI Coding for Real Engineers was created to help professional developers integrate AI coding tools into real production workflows

while preserving the engineering practices that make software reliable and scalable.

The course emphasizes that AI increases the importance of engineering fundamentals rather than eliminating them.

Core learning areas may include:

AI-assisted software engineering

Claude Code workflows

Production codebase management

Software architecture

Code quality systems

Human-in-the-loop engineering

Mastering The Explore-Build-Clear Workflow

One of the course’s core frameworks is the Explore/Build/Clear loop, a workflow designed to help developers manage context effectively

while working with coding agents.

The system encourages engineers to first understand the codebase,

then execute changes, and finally clear context to avoid confusion and degraded outputs.

Workflow concepts may include:

Codebase exploration

Context management

Task execution

Session resets

Incremental development

AI-assisted workflows

Learning How To Steer AI Agents Effectively

The course teaches that AI agents require guidance, structure, and clearly defined instructions.

Participants learn techniques such as AGENTS.md files, custom skills, and progressive disclosure methods to steer coding assistants efficiently

without wasting tokens or creating unnecessary complexity.

Steering topics may include:

AGENTS.md design

Custom skill development

Prompt engineering

Progressive disclosure

Agent memory management

Workflow optimization

Designing Codebases That AI Can Understand

The course examines how software architecture itself influences AI performance.

Clean, modular, well-documented codebases often produce better AI-generated results.

Participants learn architectural practices that make projects easier for both humans and coding agents to navigate.

Architecture topics may include:

Modular design

Repository organization

Documentation strategies

Maintainable structures

AI-friendly systems

Long-term scalability