An introduction to generative AI and LLMs, outlining their history, applications, and key concepts like tokens, embeddings, and attention mechanisms. The guide then delves into the mathematical and statistical foundations of LLMs, covering essential topics such as probability theory, linear algebra, calculus, and deep learning basics. The main focus is on practical aspects of designing and training LLMs, including data collection, data preprocessing, model architectures, training techniques, evaluation metrics, and fine-tuning. The text further explores deploying LLMs in production environment