Introduction to Large Language Models (LLMs)

Large Language Models (LLMs) are transforming how we interact with technology, yet understanding how they actually work can feel overwhelming. This course demystifies the core concepts behind these powerful AI systems without requiring a background in advanced mathematics or computer science. You will transition from a curious observer to a knowledgeable professional who understands the mechanics, use cases, and limitations of modern language models. By studying foundational definitions and current industry patterns, you will build a solid base for working with generative AI technologies. What you'll learn: Define what Large Language Models are and how they differ from traditional machine learning; Explore the foundational architecture of generative AI and transformer models; Understand the basics of prompt engineering to guide model outputs effectively; Identify the core principles of Retrieval-Augmented Generation (RAG) and how it improves model accuracy; Analyze common limitations of LLMs, including hallucinations and bias; Apply ethical AI practices and responsible deployment strategies in your work. The course begins with essential terminology and the history of language processing before moving into practical applications, prompting techniques, and modern architectural patterns like RAG. You will read structured explanations and analyze real-world scenarios to consolidate your learning. This course is designed entirely for beginners with no prior coding, machine learning, or technical background required. Start reading today to unlock the potential of generative AI and build your foundational knowledge of large language models.