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Chapter 23: Building Your Own AI Products

Turn models into products: idea validation, RICE prioritization, unit economics (LTV/CAC), retention and funnel analytics, and knowing when AI is the right tool.

Metadata

Field Value
Track Advanced
Time 8 hours
Prerequisites Chapters 1, 2, 3, 4, 5, 6, 15, 20

Learning Objectives

  • Validate ideas against a real problem before building
  • Prioritize a backlog objectively with RICE scoring
  • Model unit economics — LTV, CAC, and the LTV:CAC ratio
  • Compute CAC payback and understand why it gates growth
  • Measure retention with cohort decay curves
  • Analyze funnels to find the biggest conversion leak
  • Decide when to use AI vs a simpler, cheaper solution
  • Frame an MVP and an experiment to learn fastest

What's Included

Notebooks

Notebook Description
01_validation_and_prioritization.ipynb Idea validation and RICE-based backlog prioritization
02_unit_economics.ipynb LTV, CAC, the LTV:CAC ratio, and payback period
03_retention_and_funnels.ipynb Cohort retention curves and funnel leak analysis

Scripts

  • config.py — Chapter config: example backlog and economics assumptions
  • product_metrics.py — RICE scoring, unit economics, retention, funnel analytics

Exercises

  • Problem Set 1: Prioritize & Price — Rank a backlog by RICE and judge unit economics
  • Problem Set 2: Retain & Convert — Verify retention decay and locate the worst funnel step
  • Solutions — in exercises/solutions/ (notebooks and solutions.py for CI)

Diagrams (Mermaid)

  • product_loop.mermaid, unit_economics.mermaid

Read Online

Or try the code in the Playground.

How to Use This Chapter

Quick Start

Clone the repo, install dependencies, and open the first notebook.

git clone https://github.com/luigipascal/berta-chapters.git
cd berta-chapters/chapters/chapter-23-building-your-own-ai-products
pip install -r requirements.txt
jupyter notebook notebooks/01_validation_and_prioritization.ipynb

GitHub Folder

All chapter materials live in: chapters/chapter-23-building-your-own-ai-products/

Created by Luigi Pascal Rondanini | Generated by Berta AI