LLM & Transformers: A Practical Guide to Building, Deploying & Operating AI Large Language Models From Scratch

Engineered Intelligence: LLM and Transformer Architecture, Training and Applications

Large Language Models (LLMs) and Transformers are redefining how software is built, deployed, and operated.
But most resources either stay theoretical or treat LLMs as black-box APIs.

This book takes a different approach.

LLM & Transformers: A Practical Guide to Building, Deploying & Operating AI Large Language Models From Scratch is a comprehensive, hands-on guide designed for engineers, architects, and technical practitioners who want to understand how LLMs actually work—and how to build real systems with them.

Rather than focusing on hype or surface-level usage, this book walks you step by step through the foundations, architecture, training, optimization, and production deployment of modern LLM systems, using clear explanations, diagrams, and practical design patterns.

What You’ll Learn

In this book, you will learn how to:

• Understand what language models really are and how next-token prediction drives all LLM behavior

• Move from word embeddings to attention and understand why Transformers replaced RNNs and LSTMs

• Break down the Transformer architecture layer by layer, including self-attention, feed-forward networks, residuals, and normalization

• Train and fine-tune language models, including small-scale models you can build yourself

• Apply instruction tuning, LoRA, and fine-tuning strategies effectively

• Design and implement Retrieval-Augmented Generation (RAG) systems

• Build LLM-powered applications and agents with tools, memory, and orchestration

• Optimize inference using quantization, batching, and caching

• Deploy LLM systems in real-world environments (local, cloud, and hybrid)

• Monitor performance, manage costs, and address hallucinations, drift, and safety concerns

• Explore advanced topics and future directions, including multimodal models, memory-augmented LLMs, and emerging training paradigms

How This Book Is Different

✔ Focuses on engineering reality, not marketing demos
✔ Explains why each component exists, not just how to use it
✔ Includes architecture diagrams, workflows, and system-level thinking
✔ Treats LLMs as production infrastructure, not novelty tools
✔ Bridges the gap between theory and deployment

Whether you are building internal tools, AI products, developer platforms, or research prototypes, this book gives you the conceptual clarity and practical skills to work confidently with modern LLMs.

Who This Book Is For

• Software engineers and ML engineers

• Data scientists and AI practitioners

• Technical architects and system designers

• Advanced students and self-learners

• Anyone who wants to go beyond “prompting” and truly understand LLMs

No prior deep learning expertise is required, but a basic familiarity with programming concepts will be helpful.

Build, Deploy, and Operate LLMs with Confidence

If you want a clear, practical, and end-to-end guide to Large Language Models and Transformers—one that prepares you for real-world implementation rather than just theory—this book is your roadmap.

Start building engineered intelligence today.