Large Language Models (LLMs) have emerged as a cornerstone technology, transforming how we interact with information and redefining the boundaries of Artificial Intelligence (AI). LLMs offer an unprecedented ability to understand, generate, and interact with human language in an intuitive and insightful manner, leading to transformative applications across domains like content creation, chatbots, search engines, and research tools. This book provides an authoritative exploration of the design, training, evolution, and application of LLMs. It begins with an overview of pre-trained language models and Transformer architectures, laying the groundwork for understanding prompt-based learning techniques. Next, it dives into methods for fine-tuning LLMs, integrating reinforcement learning for value alignment, and the convergence of LLMs with computer vision, robotics, and speech processing. The book strongly emphasizes practical applications, detailing real-world use cases such as conversational chatbots, retrieval-augmented generation (RAG), and code generation. These examples are carefully chosen to illustrate the diverse and impactful ways LLMs are being applied in various industries and scenarios. Not surprisingly, LLMs are highly competent at generating computer programming language and natural language. The most popular solution in this space is Github Copilot, which was designed to assist human programmers in developing software using computer code. Since it is the most popular solution in this space, below we will look at its core capabilities as an exemplar of the types of benefits that these types of LLM-enabled applications provide. Multiple programming language support allows developers to interoperate across coding languages efficiently. This capability is most useful in full-stack or specialist-domain application development, where multiple programming languages are used for different solution components. Imagine a full-stack developer writing data handling routines in javascript for the user interface. At the same time, Copilot suggests code blocks in Python for the back-end API that serves the data to the front-end.