Awesome LLM Books

Some of us learn best by reading high quality books on technical topics.

This is a curated list of books for engineers on development with Large Language Models (LLMs).

Books:

Alphabetical list of books on LLMs. Each cover/title links to more information about the book.

Cover	Details
	AI Engineering Subtitle: Building Applications with Foundation Models Authors: Chip Huyen Publisher: O'Reilly, 2025 Star Rating: 5 on Amazon, 4.67 on Goodreads Links: Amazon, Goodreads, Publisher, GitHub Project
	Build a Large Language Model Subtitle: (From Scratch) Authors: Sebastian Raschka Publisher: Manning, 2024 Star Rating: 4.7 on Amazon, 4.64 on Goodreads Links: Amazon, Goodreads, Publisher, GitHub Project
	Build LLM Applications Subtitle: (from Scratch) Authors: Hamza Farooq Publisher: Manning, 2025 Links: Publisher
	Building LLM Powered Applications Subtitle: Create intelligent apps and agents with large language models Authors: Valentina Alto Publisher: Packt, 2024 Star Rating: 4.5 on Amazon, 3.40 on Goodreads Links: Amazon, Goodreads, Publisher, GitHub Project
	Building LLMs for Production Subtitle: Enhancing LLM Abilities and Reliability with Prompting, Fine-Tuning, and RAG Authors: Louis-François Bouchard and Louie Peters Publisher: Independently published, 2024 Star Rating: 4.5 on Amazon, 4.18 on Goodreads Links: Amazon, Goodreads, Publisher
	Creating Production-Ready LLMs Subtitle: A Comprehensive Guide to Building, Optimizing, and Deploying Large Language Models for Production Use Authors: TransformaTech Institute Publisher: Independently published, 2024 Star Rating: 4.6 on Amazon, 0.00 on Goodreads Links: Amazon, Goodreads, Publisher
	Developing Apps with GPT-4 and ChatGPT Subtitle: Build Intelligent Chatbots, Content Generators, and More Authors: Olivier Caelen and Marie-Alice Blete Publisher: O'Reilly, 2023 Star Rating: 4.2 on Amazon, 3.65 on Goodreads Links: Amazon, Goodreads, Publisher, GitHub Project
	Generative AI on AWS Subtitle: Building Context-Aware Multimodal Reasoning Applications Authors: Chris Fregly, Antje Barth and Shelbee Eigenbrode Publisher: O'Reilly, 2023 Star Rating: 4.4 on Amazon, 4.50 on Goodreads Links: Amazon, Goodreads, Publisher, GitHub Project
	Generative AI with LangChain Subtitle: Build large language model (LLM) apps with Python, ChatGPT, and other LLMs Authors: Ben Auffarth Publisher: Packt, 2023 Star Rating: 4.3 on Amazon, 3.42 on Goodreads Links: Amazon, Goodreads, Publisher, GitHub Project
	Hands-On Large Language Models Subtitle: Language Understanding and Generation Authors: Jay Alammar and Maarten Grootendorst Publisher: O'Reilly, 2024 Star Rating: 4.6 on Amazon, 4.22 on Goodreads Links: Amazon, Goodreads, Publisher, GitHub Project
	LangChain Crash Course Subtitle: Build OpenAI LLM powered Apps: Fast track to building OpenAI LLM powered Apps using Python Authors: Greg Lim Publisher: Independently Published, 2024 Star Rating: 4.2 on Amazon, 4.13 on Goodreads Links: Amazon, Goodreads, Publisher
	Large Language Models Subtitle: A Deep Dive: Bridging Theory and Practice Authors: Uday Kamath, Kevin Keenan, Garrett Somers, and Sarah Sorenson Publisher: Springer, 2024 Star Rating: 4 on Amazon, 3.50 on Goodreads Links: Amazon, Goodreads, Publisher, GitHub Project
	LLM Engineer's Handbook Subtitle: Master the art of engineering large language models from concept to production Authors: Paul Iusztin and Maxime Labonne Publisher: Packt, 2024 Star Rating: 4.6 on Amazon, 4.20 on Goodreads Links: Amazon, Goodreads, Publisher, GitHub Project
	LLMs in Production Subtitle: From language models to successful products Authors: Christopher Brousseau and Matthew Sharp Publisher: Manning, 2025 Star Rating: 3.00 on Goodreads Links: Amazon, Goodreads, Publisher, GitHub Project
	Natural Language Processing with Transformers Subtitle: Building Language Applications with Hugging Face Authors: Lewis Tunstall, Leandro von Werra and Thomas Wolf Publisher: O'Reilly, 2022 Star Rating: 4.6 on Amazon, 4.43 on Goodreads Links: Amazon, Goodreads, Publisher, GitHub Project
	Prompt Engineering for Generative AI Subtitle: Future-Proof Inputs for Reliable AI Outputs Authors: James Phoenix and Mike Taylor Publisher: O'Reilly, 2024 Star Rating: 4.4 on Amazon, 3.61 on Goodreads Links: Amazon, Goodreads, Publisher, GitHub Project
	Prompt Engineering for LLMs Subtitle: The Art and Science of Building Large Language Model–Based Applications Authors: John Berryman and Albert Ziegler Publisher: O'Reilly, 2024 Star Rating: 5 on Amazon, 0.00 on Goodreads Links: Amazon, Goodreads, Publisher
	Quick Start Guide to Large Language Models Subtitle: Strategies and Best Practices for ChatGPT, Embeddings, Fine-Tuning, and Multimodal AI Authors: Sinan Ozdemir Publisher: Addison-Wesley, 2024 Star Rating: 5 on Amazon, 3.69 on Goodreads Links: Amazon, Goodreads, Publisher, GitHub Project
	RAG-Driven Generative AI Subtitle: Build custom retrieval augmented generation pipelines with LlamaIndex, Deep Lake, and Pinecone Authors: Denis Rothman Publisher: Packt, 2024 Star Rating: 4.1 on Amazon, 4.14 on Goodreads Links: Amazon, Goodreads, Publisher, GitHub Project
	Super Study Guide Subtitle: Transformers & Large Language Models Authors: Afshine Amidi and Shervine Amidi Publisher: Independently published, 2024 Star Rating: 4.8 on Amazon, 4.67 on Goodreads Links: Amazon, Goodreads, Publisher
	The Developer's Playbook for Large Language Model Security Subtitle: Building Secure AI Applications Authors: Steve Wilson Publisher: O'Reilly, 2024 Star Rating: 5 on Amazon, 3.67 on Goodreads Links: Amazon, Goodreads, Publisher
	What Is ChatGPT Doing... Subtitle: ...and Why Does It Work? Authors: Stephen Wolfram Publisher: Wolfram Media Inc., 2023 Star Rating: 4.2 on Amazon, 3.89 on Goodreads Links: Amazon, Goodreads, Publisher

On Curation

The above list is not "all books on LLM development", instead it is filtered using the following procedure:

1. Create a master list of all known books on LLM development (amazon, goodreads, google books, etc.)
2. Read book blurb and table of contents to confirm relevance (for "engineers doing LLM development").
3. Read reviews and check star ratings for quality (quality check).
4. Read comments and discussion about the book on social (twitter/reddit/etc).
5. Acquire the ebook version of the book, if possible (final read/skim to confirm relevance and quality).
6. Final judgement call (publisher, gut check).

Note that I update the list based on newly published books and emails I received about new books. Additionally, listed star ratings are updated periodically.

Make The List Better

Do you have ideas on how we make this list more awesome?

Email any time: [email protected]