Foundations of Large Language Models

Large Language Models (LLMs) are transforming how we interact with technology, but understanding how they actually work is the first step to leveraging their power. This course demystifies the core concepts behind these models without requiring a background in complex mathematics or computer science. You will transition from a curious observer to a knowledgeable practitioner capable of explaining LLM architecture, identifying viable business use cases, and crafting precise prompts to get optimal results from AI tools. What you'll learn: 1. Understand the core architecture, training phases, and foundational terminology of Large Language Models. 2. Explore diverse real-world applications of LLMs across industries, from content generation to code assistance. 3. Apply effective prompt engineering techniques, including few-shot prompting and system role definition, to improve model outputs. 4. Learn the fundamentals of Retrieval-Augmented Generation (RAG) and how it grounds LLMs in external data. 5. Identify common limitations of generative AI, such as hallucinations, and study basic strategies for mitigation. 6. Examine responsible AI principles and ethical considerations for deploying language models safely. We begin with essential definitions and the history of language processing before moving into the mechanics of how these models generate text. You will then progress to practical prompt design frameworks and modern integration patterns through written explanations and guided analysis. This course is designed for absolute beginners, business professionals, and aspiring developers who want a solid, non-technical introduction to generative AI. No prior programming or machine learning experience is required. Start reading today to build your foundational knowledge of the technology shaping the future.