Top AI Books to Master Large Language Models and AI Engineering

Introduction

Artificial intelligence is evolving at an unprecedented pace, with large language models (LLMs) at the forefront of innovation. These models are transforming industries, automating workflows, and reshaping human interaction with technology. However, truly understanding how they work requires more than just using them.

In this blog, we explore three must-read AI books that help bridge the knowledge gap in LLMs and AI engineering. Whether you’re a researcher, engineer, or AI enthusiast, these books will provide in-depth knowledge of how LLMs are built, optimized, and deployed in real-world applications.

Why Understanding AI and LLMs Matters

AI is no longer confined to research labs – it is now a crucial part of software development, business operations, and even personal productivity. LLMs like GPT-4 and DeepSeek Coder are shaping how content is created, code is written, and knowledge is accessed.

However, simply using an AI model is not enough. Professionals in AI and machine learning must understand:

• The architecture and training process of LLMs
• How to fine-tune models for specialized tasks
• The ethical considerations and risks associated with AI deployment

If you want to gain a deeper understanding of LLMs and AI engineering, these books will guide you through theory, implementation, and real-world applications.

Book 1 – Build a Large Language Model from Scratch

Author: Sebastian Ruder

Who Should Read It?

This book is perfect for AI researchers, machine learning engineers, and developers who want to build an LLM from the ground up. If you prefer a hands-on approach to learning, this book will take you through every stage of LLM development.

What You’ll Learn

• The step-by-step process of building an LLM similar to GPT-2
• Theoretical foundations of transformers and attention mechanisms
• Pre-training and fine-tuning LLMs for specific domains
• Data privacy advantages of training custom LLMs

Key Takeaways

• The best way to understand AI is to build it yourself
• Custom LLMs can outperform general models in specialized fields like finance and healthcare
• The book provides a GitHub repository with code samples

If you’re serious about understanding how LLMs work under the hood, this book is an invaluable resource.

Book 2 – AI Engineering: Building Applications with Foundation Models

Author: Chip Huyen

Who Should Read It?

This book is aimed at AI engineers, software developers, and business professionals who want to build AI applications using foundation models. It provides practical insights into AI deployment and how companies can leverage AI technology effectively.

What You’ll Learn

• The rise of AI engineering as a new discipline
• How to integrate AI models into real-world applications
• The difference between AI development and AI engineering
• Best practices for building robust and reliable AI systems

Key Takeaways

• AI engineering is about leveraging existing models, not building new ones
• Many companies implement AI unnecessarily – understanding when AI is useful is crucial
• AI applications must be planned with clear goals, expectations, and maintenance strategies

If you’re working in a company that wants to integrate AI into its operations, this book is an essential read.

Book 3 – LLM Engineer’s Handbook

Authors: Paul Ittner & Maxime Labonne

Who Should Read It?

This book is ideal for AI engineers, developers, and data scientists who want to optimize and deploy LLM-based applications. Unlike the previous two books, this one focuses more on the practical aspects of AI deployment rather than theoretical concepts.

What You’ll Learn

• The end-to-end pipeline of LLM development
• Techniques like retrieval-augmented generation (RAG) and fine-tuning
• Cloud deployment, model monitoring, and maintenance
• LLM Ops – the operational aspects of managing AI models

Key Takeaways

• The book follows a practical example of building an LLM Twin, which mimics your writing style and personality
• Covers industry-standard tools like Docker, AWS SageMaker, MongoDB, and ZenML
• Emphasizes the importance of AI system monitoring and human feedback

If you’re looking for a hands-on guide to deploying LLM applications professionally, this book is a must-read.

Final Thoughts

These three books provide different perspectives on AI and LLMs:

• Build a Large Language Model from Scratch – For understanding how LLMs work at a deep level
• AI Engineering – For learning how to integrate AI into real-world applications
• LLM Engineer’s Handbook – For technical deployment of AI models

By reading these books, you’ll gain the knowledge needed to develop, optimize, and deploy LLM applications effectively.

FAQs

1. What is the best AI book for beginners?

If you’re new to AI, AI Engineering by Chip Huyen is a great starting point. It provides an overview of AI applications and engineering principles without diving too deep into technical complexities.

2. Do I need programming skills to read these books?

Yes, at least basic to intermediate Python knowledge is recommended, especially for Build a Large Language Model from Scratch and LLM Engineer’s Handbook.

3. Are these books suitable for AI researchers?

Absolutely! Build a Large Language Model from Scratch is particularly valuable for researchers who want to explore model architecture and training techniques.

4. Which book is best for AI engineers?

The LLM Engineer’s Handbook is the most practical for AI engineers, focusing on LLM deployment, infrastructure, and monitoring.

5. How can I get hands-on experience with AI models?

Each of these books includes GitHub repositories with real-world projects, allowing you to follow along with practical examples.