Large Language Models: From Theory to Production

This book provides a technically rigorous yet accessible guide to Large Language Models (LLMs), charting their evolution from academic research projects into critical infrastructure for industries as diverse as finance, healthcare, and law.It offers readers a strong grounding in the conceptual foundations of machine learning and deep neural networks before moving into the architectures and methods that define today’s LLMs, including Transformers, tokenization strategies, and pre-training dynamics. Building on these foundations, the volume engages with the three central frontiers of LLM research: reasoning, alignment, and deployment.It examines structured reasoning approaches such as Tree of Thoughts and multi-agent systems, explores mechanisms for responsible alignment including reinforcement learning from human feedback (RLHF) and direct preference optimization (DPO), and provides practical strategies for large-scale deployment and inference efficiency in cloud environments.Alongside