Introduction to Large Language Models and Generative AI

Generative AI is reshaping how we build software, analyze data, and communicate, but getting started requires understanding the core technology under the hood. This text-based course guides you through the fundamental concepts of Large Language Models (LLMs) without complex jargon. You will transition from a curious observer to someone who understands how these powerful models are trained, how they process language, and how to effectively direct them. By learning the mechanics of prompt engineering and modern application design patterns, you will gain the vocabulary and conceptual framework needed to work confidently with generative technologies. What you'll learn: - Understand the core architecture, training phases, and capabilities of modern Large Language Models. - Apply practical prompt engineering techniques and tuning strategies to get predictable model outputs. - Explore the fundamentals of Retrieval-Augmented Generation (RAG) to connect models with external data. - Learn how vector databases and embeddings help LLMs retrieve contextual information. - Identify key use cases, limitations, and ethical considerations of generative AI in real-world scenarios. The course begins with foundational definitions and the history of language modeling, then moves into practical prompting techniques, and concludes with an overview of modern application architectures. It is designed for absolute beginners, developers, and product managers who want to understand generative AI from the ground up, with no prior machine learning experience required. Start reading today to unlock the potential of large language models and start designing your own AI solutions.