Introduction to Generative AI: LLMs, Diffusion Models, and RAG

Generative artificial intelligence is reshaping how we create content, analyze data, and build software. Understanding how these models work under the hood is no longer just for research scientists—it is an essential skill for anyone looking to navigate the modern tech landscape. This written course provides a clear, step-by-step introduction to the core technologies powering modern AI. You will move from foundational concepts to understanding how text and image generation models function, enabling you to conceptualize and design intelligent workflows. What you'll learn: Understand the fundamental mechanics of Large Language Models and transformer architectures; Explore how diffusion models generate high-quality images from text descriptions; Apply Retrieval-Augmented Generation patterns to connect models with custom data sources; Discover the role of vector databases in managing and searching high-dimensional AI data; Learn how AI agents and chatbots coordinate complex tasks autonomously; Master prompt engineering techniques to guide model outputs predictably. The course begins with essential terminology and foundational definitions, ensuring you have a solid conceptual starting point. From there, you will progress through written explanations and technical walkthroughs covering text generation, image synthesis, and orchestration patterns. This course is designed for absolute beginners, developers, and tech enthusiasts who want a clear conceptual understanding of generative AI without needing prior machine learning experience. Start reading today to build a strong foundation in the technologies shaping the future of software and creativity.