The Workflow

The Toolchain Around AI Coding

A living course for IT professionals who are comfortable in an AI chat window and starting to build real software with it, but who are still copy-pasting between the chat and their files. The goal is to replace that loop with durable engineering workflows: version control, collaboration, CI/CD, runners, and the tools that extend AI into real systems.

Thesis: the model is the cheap, swappable part. The workflow around it is the skill that lasts. This course is deliberately model- and vendor-agnostic: whichever LLM you use, the scaffolding is the same.

This repo is the course, and it also dogfoods the course: it's version-controlled, it commits its own AI instructions file (AGENTS.md, the subject of Module 5), and each module is built on a branch and merged through review, the same motion the modules teach.

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Read it as a book

The lessons render into the Wiki as a navigable textbook (unit-by-unit sidebar, one page per module, prev/next links). The wiki is generated from modules/ and kept in sync automatically; it's build output, so read it there but edit the lessons here in modules/. See tools/ for the generator and the sync workflows.

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Who this is for

IT professionals who are fluent in an AI chat window and comfortable with ops concepts. Not beginners. If you already paste code between a chat tab and your editor and feel the friction, you are the audience. You will not be taught what a variable is; you will be taught the engineering scaffolding that makes AI-assisted work safe, shareable, and repeatable.

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How the course is built

It's a dependency chain, not a topic list. Every module assumes only what the previous ones taught, and nothing references a tool before it's been introduced. The 27 modules group into five units, plus a capstone finale.

Unit	Modules	Theme
1: Get out of the chat window	1–7	The local foundation: version control, committing the AI's config, getting the AI editing real files safely.
2: Make it shareable, reviewable, recoverable	8–12	The team layer: hosting, issues, review, collaboration, recovery.
3: Automate the checking and shipping	13–19	The pipeline: tests, CI, security scanning, containers, secrets, delivery, runners.
4: Extend the AI into your systems	20–23	The frontier: MCP, skills, securing them, existing codebases.
5: AI in the loop	24–27	Agents inside the pipeline, from assistive to autonomous, plus the evals that make it trustworthy.
Capstone	finale	One real feature taken end to end.

Durable core vs. expansion zone. Modules 1–14 are the stable foundation. From Module 15 onward is the expansion zone, where a fast-moving space keeps handing us new lessons. Volatile material lives toward the back so the front stays stable as the course grows.

See the-workflow-syllabus.md for the full module-by-module plan and the reasoning behind the sequencing.

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How git works in this course

You don't memorize git commands here. Modules 1–3 have you run the basics by hand so you build intuition (the AI is still in a browser chat). Module 4 puts the AI in your editor/CLI, and from there you direct the AI to do the git work (commit, branch, merge, revert) and verify the result. Think arithmetic by hand first, then a calculator. You learn that git is critical and how it works; the AI drives the keystrokes.

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Format and conventions

• Written lessons + interactive labs. Every module is a README you read and a lab you run at the keyboard. There are no quizzes; there's a "you're done when…" check.
• Run labs on your own machine, any OS. No sandbox or cloud account required. Where a lab needs code, it leans on Python or shell, picked per lab, kept as small as possible. The concepts are language-agnostic; the labs just need something concrete to run.
• Claude Code as the worked example. Commands and labs use Claude Code as the concrete agent (claude --version # sub your own agent); the concepts stay model- and tool-agnostic.
• GitHub is the default, not the requirement. Hosting examples use GitHub because nearly everyone will encounter it, but the course is provider-neutral and includes an optional self-hosted-forge track for on-prem and air-gapped environments.
• Self-checks only. No grading, no certification; each module ends at a concrete done-criterion.

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Repo layout

ai-workflow-course/
  README.md                 # this file
  AGENTS.md                 # committed AI instructions; dogfoods Module 5 (vendor-neutral name)
  the-workflow-syllabus.md  # the full course plan (source of truth for structure)
  _TEMPLATE.md              # the shape every module follows
  modules/
    01-the-copy-paste-problem/
      README.md
      lab/
    02-version-control-as-a-safety-net/
      README.md
      lab/
    ...
  capstone/
    README.md
  assets/                   # diagrams, images

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Status

All 27 modules and the capstone are written and reviewed. The lessons render to the Wiki as a textbook, kept in sync from modules/ by CI. Each lab is skip-friendly: copy that module's lab/start/ snapshot into a fresh tasks-app, commit, and run that lab without doing the earlier ones.