Heidelberg AICurriculum
Track 1 · Beginner
1.1

Why? Who? What?

Start here — before Lesson 1.

5 lessons 2026-08-03 AI-generated

1Overview

A course born inside a developmental-biology lab at Heidelberg University that outgrew biology fast — five short lessons on why it exists, who it now serves, and what the rest of the course actually covers.

Before Lesson 1: why this course exists, who it's written for, and what you're about to learn. Five short reads, no tools to install — the map before the journey.

1.1After this chapter you can
Why this course started in a life-sciences lab, and why it now reaches far beyond biology
Who it is written for — the real, named personas, with more added as the course grows
What the chapters, examples, and tools matrix actually cover, in real numbers
Why every lesson runs first and explains second, and why every paid tool gets a free or open-source alternative
What to expect as you go: free tools, no login walls, and a course that keeps growing
1.2How many recipes?

Nearly 6,000 worked recipes across the whole course — each one credited to where it came from, not a toy demo.

1.3How many chapters?

84 chapters across 18 tracks (and growing), from talking to a chatbot to running your own AI infrastructure.

1.4What's different?

Run first, understand second, build third — every lesson starts with something you can click, not a lecture. And for every proprietary leader, there is a free, open-source or self-host alternative.

1.5Who's it for?

Written for 12 real roles — scientist, physician, founder, small-business owner, creator, finance, investor, HR, operations, sales, support, robotics — and growing as the course adds more.

1.6What does it cost?

Every path in the course has a free, no-login way to start; you decide if and when to pay for more.

2Lessons 5

2.1 Explain why this course was created

The course began as the everyday AI needs of a developmental-biology laboratoryPDF triage, spreadsheet cleaning, internal tooling — tasks that turned out to be common far outside biology.

Describe the real‑world problem that led to the course and how it expanded beyond its original lab.

TryList the five tasks I repeat most in a typical work week. For each one, say whether an AI tool could do part of it today — and which part.

Paste it into whatever chatbot you already have open: ChatGPT, Claude, Gemini. Nothing to install, no account needed here. Keep the answer — the tracks below are organised by exactly that kind of task, so it doubles as your reading order.

  1. Read the opening paragraph that links the course to Prof Joachim Wittbrodt’s laboratory
  2. Identify the generic tasks (PDF triage, spreadsheet cleaning, internal tooling) mentioned as common across fields
  3. Note how each tool is evaluated against its actual capabilities rather than marketing claims
  • You'll see A concise summary stating that the course originated from a developmental‑biology lab’s AI needs and now covers many disciplines.
  • Takeaway Real problems in one lab can reveal universal AI challenges that shape an evolving curriculum
  • Check Which everyday lab tasks turned out to be common across other fields as well?

2.2 Identify your persona

The role picker is the Who you are dropdown in the sidebar. Pick a role once and every example gallery in the course filters to work from that field.

Match yourself to one of the defined roles so the examples you see are tailored to your work

Who you are — pick your role once here and every example gallery in the course filters to it.
  1. Open the Who you are dropdown at the top of the sidebar
  2. Choose the role closest to your job — or leave it on All roles to see everything
  3. Scroll to any example gallery and watch it re-filter to the role you picked
  • You'll see The dropdown lists All roles plus twelve roles, and every example gallery re-filters to the one you chose
  • Takeaway Examples adapt to your role without limiting access to any content
  • Check What happens to the example gallery once you pick a role, and what stays available to you?

2.3 Understand the course layout

The course is organised as chapters grouped into tracks, with a worked-recipe library and a tools matrix that puts every tool it covers side by side.

See how the chapters, examples and tool comparisons are organised across the whole programme

Which AI tool? Start from the job you have and follow it to a family — consumer chatbots, app builders, coding assistants, agent frameworks, automation tools, local models.
  1. Read the counters under the homepage hero — recipes, chapters, levels and roles — to see the breadth in the site's own live numbers
  2. Open the Tools index from the sidebar to see every tool the course covers compared side by side
  3. Scan one row: what the tool is, who it is best for, and what it costs, so you know what a comparison here actually tells you
  • You'll see The tools matrix listing every tool the course covers with its purpose, its best fit and its pricing in one row each
  • Takeaway The course is a map that lets you visualise all paths before choosing one
  • Check What does the tools matrix let you compare before you commit to a tool?

2.4 Apply the course’s two guiding rules

Two rules shape every lesson here: run first, understand second, build third, and for every proprietary leader an open-source or self-hostable option alongside it.

Create lesson cards that follow the run‑first, free‑alternative principles

TryExplain in two sentences what n8n is, then give me one automation a small team could actually run in its first week.

Paste it into any chatbot. That answer is the shape of this whole course: you run something before anyone explains it to you — and n8n is the free, self-hostable option that sits next to a paid one in every comparison here.

  1. Read Run first, understand second, build third to identify the initial runnable action
  2. Select For every proprietary leader, an open‑source or self‑host option to note the free alternative for each tool
  3. Verify Free, no login, to start by confirming the first step requires no account or payment
  • You'll see A lesson card showing a short goal, a copy‑paste prompt or click sequence, a screenshot of the result and a one‑line takeaway
  • Takeaway You learn by doing the thing, not by reading about the thing — and you always have a free path to start
  • Check Why does a lesson have you run the tool before it explains how the tool works?

2.5 Navigate lesson pages and start hands‑on sessions

A lesson page stacks its lessons in reading order and carries the pre-built n8n workflows and the examples gallery that go with them.

See how each daily lesson is laid out and launch the accompanying n8n workflow

A chapter page: the On this page rail lists every lesson in reading order, and the overview tells you what the chapter is for before you start.
  1. Click Lessons tab to display the stacked lesson titles in reading order
  2. Select a lesson title to expand its description and resources
  3. Press Open in n8n on a pre‑built workflow to load it into your workspace
  4. Switch to Examples tab to browse the gallery of reference automations
  • You'll see A chapter page showing a list of lessons, pre‑built workflows you can open, and an examples gallery below them
  • Takeaway Learning is driven by doing from the first session onward
  • Check Which control loads a pre-built workflow into your own n8n workspace?

3You’ll know it worked 5 checkable outcomes in this chapter

  • A list showing 61 chapters across 10 outcome lines is visible
  • Chapters are listed under headings that describe desired outcomes (e.g., "use a chatbot well")
  • The example gallery shows 967 entries with tags for persona and category
  • A side-by-side comparison appears whenever a lesson mentions alternative tools
  • The course outlines clear stopping points such as "use a chatbot well," "write code with an agent," and "run your own model."

4FAQ, Tips & How-to 7

one problem, one solution, one action
Tip Everyone

Outcome Map — the ten paths learners can follow

Seeing the journey map of outcomes (talk to AI, build apps, create media, write code, automate work, etc.) helps learners pick a concrete goal

Lesson → AI-generated
How-to Everyone

Can’t tell which examples fit my job

Choosing a persona instantly filters the example gallery to show content most relevant to your job

Lesson → AI-generated
How-to Everyone

You can understand how many chapters and examples exist and how they are organized before committing to a learning path

Lesson → AI-generated
How-to Everyone

You can navigate learning based on what you want to accomplish rather than remembering specific products

Lesson → AI-generated
How-to Everyone

You have access to a large, reusable set of real use-case examples that are tagged by role and category

Lesson → AI-generated
How-to Everyone

Can’t tell which of several tools fits my need

When two or more tools can solve the same problem, you can see which one best matches your situation

Lesson → AI-generated
How-to Everyone

You can stop after mastering basic chatbot use or continue through code-writing agents and self-hosted models

Lesson → AI-generated

The same set on /recipes, filtered by tool and role.

5See also

💬 Discuss this chapter

Ask, share, or report — over on the Heidelberg AI community forum.