Learn how to successfuly add AI to your product

The comprehensive email course on designing and building AI features that actually work — from first idea to validation. No technical skills needed.

What you'll learn

From AI user to AI designer

Using AI and building with it are fundamentally different. Learn how system prompts power every AI product, what makes AI non‑deterministic, and why your domain expertise is now your most valuable asset.

Why the chatbot is the wrong first step

The default AI feature is a chatbot — and usually the wrong choice. Discover why open‑ended chat creates more problems than it solves, what silent failures are, and how to design AI that delivers insights proactively.

Why "ship fast, optimize later" breaks with AI

The playbook that works for traditional software fails with AI. Learn why your power users are your least profitable, how to distinguish paper optimization from real discovery, and why experimentation before production is how you prove it works.

Building your test dataset from scratch

You don't need massive datasets to start. Learn the four data myths holding teams back, how to create realistic test cases from domain expertise alone, and the three types every dataset needs: standard, edge, and adversarial.

Your first experiment in Lovelaice

A hands‑on walkthrough: write a prompt, prepare test data, choose models across families, run everything in parallel, and review results side by side — with practical guidance on annotating responses and extracting insights.

The two levers: model selection and prompt engineering

You control two things: which model you use and what instructions you give. See real data showing 0% vs 100% accuracy on the same task, the prompting techniques proven across hundreds of experiments, and why structure beats tricks.

Inside real production system prompts

A teardown of system prompts from Notion AI and Lovable. See exactly how they apply role assignment, context, error handling, and few‑shot examples at scale — and notice: most instructions were added after testing uncovered failures.

How to evaluate your AI feature

The evaluation process most teams skip — and the one that creates the most leverage. Learn the three levels (manual review → deterministic metrics → LLM as judge), why you can't skip levels, and how we evaluated our own feature across 100+ responses.

The full picture and your next step

Everything tied together: the complete framework from first idea to production‑ready AI feature. A clear summary of every concept, technique, and decision point — and your path forward.