Acknowledgments

This book grew out of a frustration and an opportunity. The frustration: applied AI is too often taught as build a demo: a classifier that works on the tidy data, a chatbot that answers the easy question, a dashboard of green ticks. Demos persuade. They do not prove anything, and the moment a solution meets real customers, real edge cases, and real blast radii, the demo’s confidence collapses. The opportunity: AI is commoditising the very craft of building those demos, which forces the harder question: what is a human practitioner actually for when a machine can build the prototype? This book is an attempt to answer both at once: to give applied AI a readable, opinionated spine, and to take a clear stance on evidence in a world where the demo is cheap and the proof is the point.

This book began in a classroom, with readers who do not write code learning to build and evaluate applied AI for the first time. Their prototypes that broke in surprising ways, their honesty when a model they trusted failed an entire customer segment, and their moments of “oh, that’s why we evaluate” shaped every chapter. Tessera is written for them, and the discipline this book teaches (prove, don’t persuade) is the one they carry into every solution they build and ship. The book now stands on its own; where its path happens to align with a course, that is a happy coincidence rather than a requirement.

This book sits in a small family, and the family made it better. Conversation, Not Delegation taught the method of working with AI as a thinking partner; Substantiate, Don’t Assume taught the audit sibling of this same instinct. Prove, Don’t Persuade is the build-and-evaluate companion to both. Where those books ask how do we work with the machine? and what do we verify?, this one asks the question that lands on every applied-AI practitioner’s desk: does it actually work, and how do you know?

Colleagues who reviewed early drafts and pushed back on arguments that were not yet strong enough made the book sharper. You know who you are, and I am grateful.

The open-source community behind Quarto, Pandoc, and GitHub made it possible to write, build, and publish this book with tools that are free and transparent. The open tools the book itself teaches (Orange Data Mining, Ollama, Open WebUI, Teachable Machine, and NotebookLM) are what make no-code, equity-first applied AI possible at all. Those tools being free and capable is the enabling condition for everything in these pages.

This book was written using the methodology its companion, Conversation, Not Delegation, describes. AI was involved at every stage: as a drafting partner, a sounding board, and a source of plausible-sounding mistakes that needed catching. A practitioner’s relationship to AI-generated work is exactly what this book teaches: the machine produces the draft, and the demo; the human supplies the judgement, the evidence, and the defensible answer to does it actually work? Every sentence here reflects the author’s judgement. The AI made the work faster without doing the thinking or the proving.