AI Dev
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Course map

Choose a project route or open the smallest reference lesson needed for the step in front of you.

You do not need to read this course from top to bottom.

Start with a project

Open a reference when a project asks for it

  1. What AI is — models, training, tiers, context, cost, prompting, retrieval, and fine-tuning.
  2. Where you use it — chat, agentic workspaces, connectors, permissions, APIs, and billing.
  3. The machine — terminal, editor, Node, packages, Git, GitHub, Markdown, assets, hosting, and databases.
  4. Project: a landing page — a complete prompt-first website route.
  5. Project: an app — a reusable PRD-to-production app route.
  6. Doing it well — approval gates, secrets, context, evals, cost, rollback, and maintenance.

The infographic library collects all 31 course visuals and links each one to its production prompt and review record.

For instructors

lesson

Start here: pick a route and check the ground

Choose the landing-page or app route and prove that its accounts, source material, and local tools are ready.

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Part 1 — What AI is

Learn the smallest set of AI concepts needed to choose a model, prepare context, and select an adaptation method.

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Part 2 — Where you use it

Compare AI surfaces by context, tools, permissions, persistence, and billing before choosing where work should happen.

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Part 3 — The machine

Learn the terminal, editor, runtime, Git, GitHub, assets, and web-service layers needed by the two projects.

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Project: a landing page

Build a sourced Astro landing page, review it locally, and deploy the reviewed commit through GitHub and Vercel.

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Project: an app

Turn a learner-chosen app idea into one secure, authenticated, tested, and deployed vertical slice.

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Doing it well

Use human gates, safe configuration, focused context, evidence, usage signals, and rollback points throughout either project.