Foundations of Large Language Models and Generative AI

Generative artificial intelligence is transforming how we interact with technology, yet understanding the underlying mechanics of Large Language Models (LLMs) can feel overwhelming. This course demystifies the core concepts of LLMs, helping you build a solid theoretical foundation without getting lost in complex mathematics. You will transition from a curious observer to a knowledgeable practitioner capable of explaining how language models work, identifying their best use cases, and applying practical techniques to steer model outputs effectively. What you'll learn: Understand the foundational architecture, training processes, and capabilities of modern Large Language Models; Explore practical use cases for LLMs across various industries and business workflows; Master the fundamentals of prompt engineering and prompt tuning to optimize model performance; Learn the basic concepts of Retrieval-Augmented Generation (RAG) and how vector databases enhance LLM accuracy; Identify ethical considerations, limitations, and safety guidelines when deploying generative AI. The course begins with essential terminology and the evolution of language modeling before moving into practical prompt strategies and modern architecture patterns. You will read structured explanations, analyze real-world scenarios, and examine illustrative text-based examples. This course is designed for absolute beginners, business professionals, and aspiring developers who want to understand generative AI from the ground up, with no prior programming or machine learning experience required. Start your journey into the world of generative AI today.