Generative AI Fundamentals

Generative AI is reshaping how we write, design, code, and solve problems — but most explanations either stay too abstract or jump straight into jargon without building real understanding. This course closes that gap.

Across 6 comprehensive practice tests and 600 carefully explained questions, you'll build a genuinely well-rounded foundation in generative AI, starting from the ground up. You'll begin with the core concepts and terminology that define this field, then explore the major model architectures powering today's tools — GANs, VAEs, diffusion models, and transformers — and understand what makes each one work.

From there, you'll dive into large language models specifically: how tokenization, context windows, and sampling shape the text these models generate, along with their genuine strengths and real limitations. You'll then look at how generative AI is actually being applied across industries — from content creation to coding to healthcare — before turning to one of the most important parts of working with this technology responsibly: understanding its risks, limitations, and the ethical considerations every practitioner should know.

Finally, the course turns fully practical. You'll learn how to choose the right tools, write clear and effective prompts, apply advanced techniques like few-shot prompting, chain-of-thought reasoning, and role-based prompting, and troubleshoot common issues — building the hands-on confidence to start using generative AI effectively right away.

No coding or AI background is required. Every question comes with a detailed explanation, so you're not just testing yourself — you're learning as you go. Whether you're a complete beginner, a student preparing for interviews, or a professional looking to use these tools more effectively, this course gives you a solid, practical foundation in generative AI.