How to Use This Book

The two halves: chapters and labs

The book has two halves that travel together.

Chapters (Parts I–VII) are the narrative — read them. They build a single arc from “what is an LLM” through chat workflows, the terminal, agentic AI, text as data, AI in empirical research, APIs, and a full-stack capstone project. Read them in order the first time. Skim and reference them later.

Labs (Part VIII) are the practice — work through them. Each lab is a self-contained exercise drawn from the original course, deliberately stripped down: a brief, a dataset, a prompt or two, and a deliverable. Labs preserve the original course material as-is so you can do them on your own without an instructor in the room. Many labs map 1-to-1 to the weekly assignments of the course.

A typical study unit:

Read one or two chapters.
Do the matching lab.
Write your “AI and me” reflection at the end of the lab.

Three reader profiles

Self-learner. Start at the preface, read straight through, do every lab. Plan on roughly two evenings per chapter for the early parts and a full weekend per part once you hit the CLI material in Part III. The capstone in Part VII expects you to set aside three full sessions of a few hours each.

Student in a course. Your instructor will assign chapters and labs in some order. The book’s linear arc is one valid path; your syllabus is another. Use the chapters as preparation reading and the labs as homework.

Instructor. Fork the repo. Re-mix chapters into the rhythm of your class; the labs were originally weekly assignments and still work as such. The website version (sidebar by week) is a second view of the same material, also in the repo. License is CC BY-NC-SA 4.0 — see the appendix for attribution.

What you need before you start

A working Python setup — Anaconda, uv, or your preferred manager. The book is Python-only; see the preface for why.
An account with at least one frontier chat model. This edition was written against Claude Opus 4.7 and ChatGPT 5.5; either works for almost every chapter, with occasional notes on where they differ. See Edition and Model Snapshot.
For Part III onward: a terminal you are willing to live in (macOS Terminal / iTerm, Windows PowerShell or WSL, any Linux shell).
For Parts III and VI: an API key for at least one LLM provider. Chapter 25 walks you through getting one without overpaying.

A note on AI in your work

The course has one unbreakable rule and the book inherits it:

Use AI freely. Do not submit something created by AI. AI is your assistant, not your ghostwriter.

In every lab you should be able to defend every line of code and every claim in your write-up. If you cannot, you have over-delegated. The point of the labs is to practice exactly this judgement.

At the end of each chapter and lab, three questions:

How did AI support me in doing what I planned?
How did AI fail me — half-truths, buggy code, imprecise arguments?
How did AI extend me — letting me do things I couldn’t, or giving me new ideas?

Write the answers down. They are the most important artefact you will produce.

Conventions

Code blocks are Python, end of story. Principles transfer to R, Stata, or Julia; the code in this edition does not.
Callouts mark places where the AI tooling is most likely to mislead you, and where human judgement matters most.
Footnotes point to the original course-website page when a chapter is a re-flow of one — useful for instructors looking for the source.
Case studies (World Values Survey, Austrian Hotels, football interviews, US earnings, employee commits) recur across chapters. The Reference part lists them with one-page summaries.
Many short chapters. The book deliberately favours short, single-idea chapters over long bundled ones — they read better on the web and re-mix more easily for instructors.

--- title: "How to Use This Book" --- # How to Use This Book {.unnumbered} ## The two halves: chapters and labs The book has two halves that travel together. **Chapters (Parts I–VII)** are the *narrative* — read them. They build a single arc from "what is an LLM" through chat workflows, the terminal, agentic AI, text as data, AI in empirical research, APIs, and a full-stack capstone project. Read them in order the first time. Skim and reference them later. **Labs (Part VIII)** are the *practice* — work through them. Each lab is a self-contained exercise drawn from the original course, deliberately stripped down: a brief, a dataset, a prompt or two, and a deliverable. Labs preserve the original course material as-is so you can do them on your own without an instructor in the room. Many labs map 1-to-1 to the weekly assignments of the course. A typical study unit: 1. Read one or two chapters. 2. Do the matching lab. 3. Write your "AI and me" reflection at the end of the lab. ## Three reader profiles **Self-learner.** Start at the preface, read straight through, do every lab. Plan on roughly two evenings per chapter for the early parts and a full weekend per part once you hit the CLI material in Part III. The capstone in Part VII expects you to set aside three full sessions of a few hours each. **Student in a course.** Your instructor will assign chapters and labs in some order. The book's linear arc is one valid path; your syllabus is another. Use the chapters as preparation reading and the labs as homework. **Instructor.** Fork the repo. Re-mix chapters into the rhythm of your class; the labs were originally weekly assignments and still work as such. The website version (sidebar by week) is a second view of the same material, also in the repo. License is CC BY-NC-SA 4.0 — see the appendix for attribution. ## What you need before you start - A working **Python** setup — Anaconda, `uv`, or your preferred manager. The book is Python-only; see the preface for why. - An account with at least one frontier chat model. This edition was written against **Claude Opus 4.7** and **ChatGPT 5.5**; either works for almost every chapter, with occasional notes on where they differ. See [Edition and Model Snapshot](versions.qmd). - For Part III onward: a terminal you are willing to live in (macOS Terminal / iTerm, Windows PowerShell or WSL, any Linux shell). - For Parts III and VI: an API key for at least one LLM provider. Chapter 25 walks you through getting one without overpaying. ## A note on AI in your work The course has one unbreakable rule and the book inherits it: > Use AI freely. Do not submit something *created* by AI. AI is your assistant, not your ghostwriter. In every lab you should be able to defend every line of code and every claim in your write-up. If you cannot, you have over-delegated. The point of the labs is to practice exactly this judgement. At the end of each chapter and lab, three questions: 1. How did AI **support** me in doing what I planned? 2. How did AI **fail** me — half-truths, buggy code, imprecise arguments? 3. How did AI **extend** me — letting me do things I couldn't, or giving me new ideas? Write the answers down. They are the most important artefact you will produce. ## Conventions - **Code blocks** are **Python**, end of story. Principles transfer to R, Stata, or Julia; the code in this edition does not. - **Callouts** mark places where the AI tooling is most likely to mislead you, and where human judgement matters most. - **Footnotes** point to the original course-website page when a chapter is a re-flow of one — useful for instructors looking for the source. - **Case studies** (World Values Survey, Austrian Hotels, football interviews, US earnings, employee commits) recur across chapters. The Reference part lists them with one-page summaries. - **Many short chapters.** The book deliberately favours short, single-idea chapters over long bundled ones — they read better on the web and re-mix more easily for instructors.