LLM Engineering: Prompting, Fine-Tuning, and RAG

Large Language Models (LLMs) offer immense potential, but unlocking their full power requires specific engineering techniques. This course provides a clear, text-based path to understanding how to effectively build and optimize LLM applications. By the end of this course, you will be equipped with the fundamental knowledge and practical skills to design, develop, and refine robust LLM-powered solutions, moving beyond basic interactions to create more reliable and accurate AI systems. What you'll learn: * Understand the fundamental concepts and architecture of Large Language Models. * Apply effective prompt engineering techniques, including advanced patterns for complex tasks. * Configure and evaluate Retrieval Augmented Generation (RAG) systems to improve LLM accuracy and context. * Practice the principles of fine-tuning LLMs for specialized applications. * Implement basic strategies for optimizing LLM performance and resource usage. * Develop a foundational understanding of how to evaluate LLM outputs and system reliability. The course begins with essential LLM terminology and concepts, then progresses through practical applications of prompting, fine-tuning, RAG, and optimization techniques. You will learn by reading clear explanations and practicing through written exercises and code snippets. This course is designed for beginners interested in building with Large Language Models. No prior experience with LLM engineering or advanced AI concepts is required, only a basic understanding of programming fundamentals. Start your journey to becoming proficient in LLM engineering today.