Understand what sets open generative AI models apart. In this course, you’ll learn how to spot license limitations, break down the key components of large language and diffusion models, and compare performance trade-offs like parameter size, speed, and accuracy. You’ll also apply a simple decision framework that helps you choose the right model for your needs, building the confidence to make smart, compliant, and cost-effective choices.

In this module, you’ll learn how to tell what you can and can’t do with open generative AI models. We’ll break down the differences between open source, open weights, and open access, compare common license types, and show how each affects commercial use. You’ll also practice spotting legal red flags, understanding attribution requirements, and applying compliance best practices so you can avoid costly mistakes and deploy models with confidence.

In this module, you’ll learn the fundamentals that shape how open models are built and perform. You’ll explore the core components of transformer architecture, compare major models like LLaMA and Mistral, and understand the principles behind diffusion models. You’ll also evaluate how parameter size, context windows, and inference speed trade off against each other, so you can make informed choices about which model fits your needs.