Large Language Models: Scaling, Reasoning, and Efficiency

As artificial intelligence continues to advance, understanding how large language models scale and reason is essential for building efficient applications. This written course demystifies the complex architectures behind modern AI systems without requiring a background in advanced mathematics. You will transition from a curious observer to a knowledgeable practitioner capable of explaining and applying advanced LLM concepts. Through clear explanations, you will grasp how modern models achieve high performance while managing computational costs in real-world environments. What you'll learn: - Understand foundational LLM terminology, basic transformer concepts, and scaling laws. - Explore Mixture of Experts (MoE) architectures to understand how models route tasks efficiently. - Analyze reasoning models and chain-of-thought prompting patterns that improve decision-making. - Discover Retrieval-Augmented Generation (RAG) patterns and the role of vector databases in extending model knowledge. - Apply prompt engineering basics to optimize model outputs for practical use cases. The course begins with core definitions and basic architectures before progressing to advanced scaling techniques and real-world implementation strategies. You will read through conceptual explanations and practical text-based examples designed to build your confidence step-by-step. This text-only course is designed for beginners, developers, and tech enthusiasts who want to understand modern AI infrastructure with no prior machine learning experience required. Start reading today to master the core principles of scaling and reasoning in large language models.