Artificial Intelligence is reshaping industries, creating opportunities for anyone with the right skills. But you don’t need a $200K master’s degree to break into this field. The smartest path is often self-learning with the right resources. In this blog, we’ll explore 7 AI Books that not only explain core concepts but also give you hands-on projects, GitHub repositories and real-world applications. Whether you’re just starting out or looking to sharpen your engineering skills, these books can help you build practical expertise in AI from the ground up.
These 7 AI Books form a complete AI engineering curriculum from building transformers from scratch to deploying large-scale LLM systems. Several come with GitHub repositories so you can code along as you learn.
1. AI Engineering
Chip Huyen’s AI Engineering is a hands-on guide for building applications with foundation models. Drawing from her experience at Netflix and NVIDIA, the book covers the full stack-data pipelines, deployment, monitoring, versioning, RAG and fine-tuning while addressing real production challenges like latency, cost and reliability. Unlike many theory-heavy resources, this book focuses on practical systems design, showing how to take LLMs from prototype to production. It’s an essential read for engineers, data scientists and product managers who want to build scalable, reliable AI applications.
2. Designing Machine Learning Systems
Chip Huyen’s Designing Machine Learning Systems is a practical blueprint for building ML systems that are reliable, scalable and production-ready. Rather than focusing on coding tutorials, it emphasizes the critical design decisions covering data pipelines, model deployment, monitoring, retraining and responsible AI. The book highlights why ML systems break in real-world environments and how to catch issues before users do. Translated into multiple languages and praised by leaders at Google, Slack, and Made With ML, it’s an essential read for engineers, data scientists and technical leaders who want to deploy ML at scale.
3. Build a Large Language Model from Scratch
Sebastian Raschka’s Build a Large Language Model (From Scratch) is a hands-on deep dive into how modern LLMs work under the hood. Instead of relying on prebuilt libraries, this book walks you step by step through implementing attention, transformers, GPT-style architectures, pretraining and finetuning – all using PyTorch from the ground up. The approach mirrors how large-scale foundation models like GPT are trained but is designed to run on standard hardware, making it accessible for learners and practitioners. By the end, you’ll not only understand the theory but also have working code that builds and finetunes your own functional LLM.
📖 Amazon – Build a Large Language Model From Scratch
💻 GitHub Repo
4. LLM Engineer’s Handbook
The LLM Engineer’s Handbook is a comprehensive, end-to-end guide for building, deploying and maintaining production-ready LLM systems. It walks you through every stage of the lifecycle-data collection, model training, RAG integration, AWS deployment, monitoring, testing and evaluation using modern MLOps practices. The book emphasizes practical engineering, showing you how to build scalable applications with tools like ZenML, Qdrant, MongoDB, AWS SageMaker and Hugging Face. By the end, you’ll have a working blueprint for taking an LLM project from prototype to production.
📖 Amazon – LLM Engineer’s Handbook
💻 GitHub Repo
5. Building LLMs for Production
Building LLMs for Production is a hands-on, 463-page guide to adapting and deploying large language models for real-world use cases. Written by a team of experts from Towards AI, Activeloop, LlamaIndex and Mila, the book focuses on making LLMs accurate, reliable, and scalable in production. It covers everything from prompting and RAG to fine-tuning, agents, deployment and cost optimization. With Colab notebooks, real-world code projects, and community support, this resource is ideal for developers who want to move beyond prototypes and deliver production-grade LLM systems.
📖 Amazon – Building LLMs for Production
6. Hands-On Large Language Models
Known as The Illustrated LLM Book, this resource combines clear explanations, nearly 300 custom visuals, and hands-on code to make large language models accessible. From tokens and embeddings to semantic search, RAG, multimodal LLMs and fine-tuning, the book balances theory with practice. Endorsed by AI leaders like Andrew Ng and Nils Reimers, it’s designed for learners who want to understand, experiment and build with modern LLM stacks. All chapters come with runnable Colab notebooks, making it easy to apply concepts right away.
📖 Amazon – Hands-On Large Language Models
💻 GitHub Repo
7. Prompt Engineering for LLMs
Prompt Engineering for LLMs is a practical guide to mastering the art and science of communicating with large language models. The book explains how to design effective prompts, manage context, and apply advanced strategies such as few-shot prompting, chain-of-thought reasoning and RAG. Beyond quick hacks, it teaches you how to build robust prompt-crafting strategies for production-ready applications. With clear explanations and actionable techniques, this book equips developers and product builders to unlock the full potential of LLMs in real-world systems.
📖 Amazon – Prompt Engineering for Large Language Models
Final Thoughts
The journey to becoming an AI engineer is no longer limited to classrooms and expensive graduate programs. With these 7 AI Books, you can create your own roadmap – one that blends theory, practice, and the latest breakthroughs in large language models and machine learning. Each book is a step closer to mastering AI and applying it in real-world projects. Start with one, build consistently and you’ll have the skills to thrive in one of the most exciting fields of our time.
These seven books together cover:
- Core theory (building models from scratch)
- Practical tools (Hugging Face, LangChain etc.)
- Deployment, scaling and monitoring
- Business and leadership aspects
All for the cost of a few hundred dollars not a $200K master’s.
That’s how you become an AI engineer.
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