Foundations of Generative AI: Training, Evaluation, and Modern Patterns

Generative artificial intelligence is transforming how we build software, analyze data, and solve complex problems. To leverage this technology effectively, you must understand the underlying principles of how these models function, train, and evolve. This text-based course guides you from absolute beginner to a solid conceptual understanding of generative AI mechanics. You will learn the core architectures behind large language models, how they process information, and the modern methodologies used to evaluate and deploy them safely. What you'll learn: - Understand the foundational concepts of generative AI, neural networks, and transformer architectures. - Learn how large language models are trained, pre-tuned, and aligned using modern reinforcement learning techniques. - Explore modern implementation patterns including Retrieval-Augmented Generation (RAG) and vector databases. - Master key evaluation metrics to assess model performance, accuracy, and bias. - Practice basic prompt engineering techniques to guide model outputs effectively. - Examine current trends, ethical considerations, and safety guardrails in AI deployment. We begin with fundamental definitions and core terminologies before diving into transformer mechanics, training workflows, and modern system architectures. Designed for beginners with no prior machine learning experience, this course is perfect for developers, product managers, and tech enthusiasts looking to build a strong conceptual foundation. Start reading today to demystify generative AI and prepare for the future of technology.