Books
Free books
Foundations of Large Language Models by Tong Xiao and Jingbo Zhu book
paid books
1) “Hands-On Large Language Models” by Jay Alammar & Maarten Grootendorst - Excellent practical guide, Amazing Illustrations and code exmplaes GitHub
2) “Build a Large Language Model (From Scratch)” by Sebastian Raschka, PhD - Perfect for understanding core architecture. Goes into details also about e.g. data preprocessing.
3) “Building LLM Powered Applications” by Valentina Alto - Great for real-world implementation, heavy focus on using open-source libraries like LangChain
4) “Natural Language Processing with Transformers” by Lewis Tunstall, Leandro von Werra & Thomas Wolf - Essential for understanding the backbone of modern LLMs. Naturally Hugging Face focused, which is good.
5) “Understanding Deep Learning” by Simon Prince - Amazing theoretical foundation. Has the most math, but explained in very friendly way and easy to follow step-by-step.
📚 Core LLM & GenAI
- The LLM Book by Andriy Burkov
- LLM Engineer’s Handbook by Paul Iusztin, Maxime Labonne
- Building LLMs for Production by Louis-François Bouchard, Louie Peters
- Decoding GPT by Devesh Rajadhyax
- Introduction to Python and Large Language Models by Dilyan Grigorov
- The Complete Obsolete Guide to Generative AI by David Clinton
- Generative AI in Action – Amit Bahree, Manning 2024 – practitioner’s guide to building GenAI text, image and code.
- Generative AI Handbook: A Roadmap for Learning Resources by William Brown
- Generative AI Made Easy – Andrea De Mauro, Manning (MEAP 2025) – low-code, illustrated walkthrough from idea to production
🤖 AI Agents
- AI Agents in Action by Michael Lanham
- Agents in the Long Game of AI – Marjorie McShane et al., MIT Press 2024 – hybrid AI architecture for trustworthy agents
- The AI Agent Mandate: Reimagining Work in the Age of AI – Marco Buchbinder, Berrett-Koehler 2025 – how autonomous agents reshape organisations
🔬 Machine Learning
- The Hundred-Page ML Book by Andriy Burkov - Amazing intro-to-ML book. Anytime I have an non-ML leader who is interested in AI, I give them this book.
- Deep Learning with Python by François Chollet - Amazing hands-on approach to DNNs. My recommendation to any engineer who want to get familiar with pre-LLM architectures.
- The Little Book of Deep Learning by François Fleuret
- Inside Deep Learning by Edward Raff
- Neural Networks: Zero to Hero by Andrej Karpathy
🧮 Math & Foundations
- Essential Math for AI by Hala Nelson
- Causal Inference in Python by Matheus Facure
- Alice’s Adventures in a differentiable wonderland by Simone Scardapane
🎯 Prompt Engineering
- Prompt Engineering for LLMs by John Berryman & Albert Ziegler
- Prompt Engineering for Generative AI by James Phoenix & Mike Taylor
- Prompt Engineering in Action – Shivendra Srivastava & Naresh Vurukonda, Manning (MEAP 2025) – reusable prompt patterns, RAG & agent tips GitHub
- Prompt Engineering in Practice – Richard Davies, Manning (in development 2024) – prompt design, evaluation & scaling techniques
⚙️ Engineering & Security
- Machine Learning Engineering by Andriy Burkov
- The Developer’s Playbook for LLM Security by Steve Wilson
- AI Engineering by Chip Huyen - One of my favorite authors on anything LLM eng related. I have this book on pre-order.
- Generative AI on AWS by Chris Fregly, Antje Barth & Shelbee Eigenbrode GitHub
📱 Hands-on Learning
- Hands-on-LLM repo by Jay Alammar & Maarten Grootendorst
- AI A-Z 2024 by Hadelin de Ponteves, Hon. PhD & Kirill Eremenko
📝 Essential Blogs
- Chip Huyen’s Blog by Chip Huyen - again, this blog is gold. I love Chip’s books and articles.
- AI by Hand by Tom Yeh
🛠️ Standards & Documentation
- Training Data Markup Language for AI by OGC
⚖️ Ethics, Society & Critique
- The AI Con: How to Fight Big Tech’s Hype and Create the Future We Want – Emily M. Bender & Alex Hanna, Allen Lane 2025 – critical look at LLM hype and ethics
- Empire of AI – Karen Hao, 2025 – reportage on OpenAI & power dynamics in the GenAI era
This page was originally adapted from Lukas Tencer’s LinkedIn blog post and has been expanded by Fatemeh Rahimi with further research (updated 25 May 2025).