Course Room
Beginner to Advanced

AI Product Mastery: Build, Launch, and Scale AI-Powered Products

A complete, power-packed AI product management course: master how LLMs actually work, then level up with context engineering, RAG, prompt engineering, fine-tuning, AI agents, MCP, and real evaluation methods, through live, hands-on case studies.

8 Modules
24 Lessons
0 Students
English
Certificate on completion

AI Product Mastery: Build, Launch, and Scale AI-Powered Products

Course Overview

AI Product Mastery takes you from the fundamentals of how large language models are actually built, pre-training, tokenization, transformers, and post-training, through to the practical skills that define a working AI product manager. You'll master the full AI value chain and the five-angle product framework through a real meeting-assistant case study, then go deep on context engineering: RAG, prompt engineering, and fine-tuning, including a live NotebookLM demo and a hierarchical RAG problem-solving walkthrough. From there, you'll learn transfer learning, build a real working AI agent live (a LinkedIn summarizer that emails you automatically), understand the full agent stack and guardrails, see exactly how vibe-coding tools like Lovable work under the hood, learn MCP so agents can talk to any tool, and evaluate AI products using the LLM-as-a-judge method through an AI-powered job website case study. The course closes with concrete, actionable advice for building a real AI product portfolio and breaking into the field.

8 modules
24 lessons
8 module quizzes

Taught in English
Beginner to Advanced level
Certificate on completion

Course Content

8 modules24 lessons8 quizzes

Introduction and the Gen AI Revolution6:13
Understanding Large Language Models2:43
Tokenization and Training Data5:08

Module quiz · 30 questions

The Stages of Training a Large Language Model10:42
Transformers and the Role of GPUs13:55
Post-Training and Reducing Hallucinations5:35

Module quiz · 30 questions

The AI Product Management Value Chain12:45
Case Study: AI Note Takers, User and Business Value5:58
Designing for Trust, and How AI Meeting Tools Actually Work10:28
Growth Strategies, and the Limits of Just Adding More Context6:13

Module quiz · 30 questions

Introduction to Context Engineering and RAG18:56
How RAG Actually Answers a Question16:24
Prompt Engineering Essentials and a Guide for Busy People14:46

Module quiz · 30 questions

Day Two Consolidation: RLHF and Sharper Prompting Techniques12:15
Context Engineering and RAG in Practice: A Live NotebookLM Demo25:11

Module quiz · 30 questions

Fine-Tuning: The Third Pillar of Context Engineering3:16
Introduction to Transfer Learning3:45
Creating and Utilizing AI Agents8:35

Module quiz · 30 questions

Building a Real AI Agent: LinkedIn Summaries and Email Automation3:10
The AI Agent Stack: Reasoning, Memory, Tools, and Guardrails2:53
How Vibe-Coding Tools Like Lovable Actually Work4:56

Module quiz · 30 questions

MCP: Letting AI Agents Talk to Any Tool12:30
Evaluating AI Products: The Job Website Case Study9:30
From Information to Knowledge: Building a Strong AI Portfolio5:32

Module quiz · 30 questions

Skills You'll Gain

Explain how large language models are pre-trained, tokenized, and post-trained
Apply a five-angle product framework to an AI product case study
Apply context engineering techniques including RAG and prompt engineering to a product

Explain fine-tuning and transfer learning trade-offs for AI products
Design and connect AI agents to external tools
Evaluate an AI product's performance and plan next steps in an AI PM career

Requirements

Basic familiarity with core product-management concepts (what a product roadmap, user story, or stakeholder is) is genuinely expected going in
this course does not teach product management fundamentals from zero, only how AI changes the practice. No AI/ML background is required.