Docs · Getting started

Day-zero walkthrough

Five steps from your first login to your first finished lab. Roughly 90 minutes end-to-end if you don't pause.

Step 1

Pick a discipline

A discipline is a career track — for example, GenAI Engineering or AI Security Engineering. Each one bundles 2–4 courses and 24–48 chapters of focused work.

Open the Disciplines page to browse what's available.

Disciplines landing page showing six discipline cards
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The Disciplines page · click any card to see its courses.

Step 2

Enroll in a course

Each discipline has multiple courses. A course is the right size for a 3–4 week sprint of learning — typically 12 chapters totaling around 18 hours of content. You'll see chapter previews and time estimates before you commit.

Once you're enrolled, the course shows up in your Progress dashboard and on the home page as a "Continue" card.

Step 3

Open your first chapter

Every chapter has the same four parts: reading material, audio overview, quiz, and lab. The recommended order is read → audio → quiz → lab, but you can jump between them freely. Progress saves as you go.

Chapter page showing the four artifact tabs
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A chapter page — the same layout in every course.

Step 4

Run your first lab

Click the Lab tab. After ~30 seconds, a real VS Code workspace opens in your browser. Each lab has 1–3 TODOs marked clearly in the code. Implement them, run the tests with the play button, and iterate until they pass.

Tests run on GenBodha's cloud — no local setup, no Docker, no dependency hell. The lab persists for 24 hours so you can come back to it.

Lab workspace with VS Code editor and pytest output
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The lab workspace · file tree on the left, editor in the middle, terminal at the bottom.

Step 5

Track progress

Your progress dashboard shows everything: chapters complete, hours learned, quiz accuracy, lab pass rate. Use it to see what to do next and how far you've come.

Progress dashboard with stats, active courses, and activity heatmap
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The Progress page · stats, active courses, and a 12-week activity heatmap.