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AI/LLM Books - Snickerdoodle - 09-14-2026
AI / LLM Recommended Reading List
Here's my collection of essential AI and Large Language Model books organized by tier.
Must Read
1. AI Engineering: Building Applications with Foundation Models
Author: Chip Huyen
Synopsis: A practical guide focusing on the architectural principles, patterns, and best practices required to design, build, and deploy production-ready applications powered by foundation models and LLMs.
Spoiler:
2. Build a Large Language Model (From Scratch)
Author: Sebastian Raschka
Synopsis: Step-by-step instructions on creating a fully functioning LLM from the ground up using PyTorch, covering tokenization, self-attention mechanisms, pre-training, and fine-tuning.
Spoiler:
3. Hands-On Large Language Models
Author: Jay Alammar & Maarten Grootendorst
Synopsis: An accessible, visually rich introduction to understanding, implementing, fine-tuning, and using LLMs for real-world Natural Language Processing tasks.
Spoiler:
4. Designing Machine Learning Systems
Author: Chip Huyen
Synopsis: A comprehensive, end-to-end framework for designing machine learning systems that are reliable, scalable, maintainable, and adaptable to real-world production environments.
Spoiler:
5. Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow
Author: Aurélien Géron
Synopsis: A concrete, hands-on guide using practical Python tools to understand machine learning concepts and build deep neural networks.
Spoiler:
Solid Book
6. LLM Engineer’s Handbook
Author: Paul Iusztin & Maxime Labonne
Synopsis: An end-to-end practical manual for building, fine-tuning, evaluating, and serving large language models at scale in production systems.
Spoiler:
7. Designing Multi-Agent Systems
Author: Victor Dibia
Synopsis: Explores principles, patterns, and practical code implementations for building collaborative, autonomous AI agent teams to solve complex tasks.
Spoiler:
8. Building Applications with AI Agents
Author: Michael Alcorn
Synopsis: Focuses on designing, building, and deploying autonomous and multi-agent systems using modern frameworks and tools.
Spoiler:
9. Natural Language Processing with Transformers
Author: Lewis Tunstall, Leandro von Werra, & Thomas Wolf
Synopsis: A hands-on walkthrough on using Hugging Face Transformers to build, fine-tune, and deploy state-of-the-art NLP models.
Spoiler:
10. Designing Data-Intensive Applications
Author: Martin Kleppmann
Synopsis: A definitive technical deep-dive into the core principles, trade-offs, and architectures behind reliable, scalable, and maintainable data storage and processing systems.
Spoiler:
Good Read
11. Prompt Engineering for LLMs
Author: John Berryman & Albert Ziegler
Synopsis: Details the techniques, art, and science behind effective prompt formulation to build resilient language model applications.
Spoiler:
12. Building LLMs for Production
Author: Towards AI (Louie Peters et al.)
Synopsis: A practical guide covering prompt engineering, RAG, fine-tuning, and model evaluation to scale modern AI solutions into robust production environments.
Spoiler:
13. AI Agents in Action
Author: Lanham
Synopsis: Focuses on building, configuring, and deploying practical AI agents capable of autonomous decision-making and workflow execution.
Spoiler:
14. Prompt Engineering for Generative AI
Author: James Phoenix & Mike Taylor
Synopsis: Teaches practical prompt design strategies, structured inputs, and optimization techniques for repeatable, reliable generative AI outputs.
Spoiler:
15. The Hundred-Page Language Models Book
Author: Andriy Burkov
Synopsis: A concise, highly accessible introduction explaining how language models work, complete with code snippets using PyTorch.
Spoiler:
Read Later
16. Deep Learning
Author: Ian Goodfellow, Yoshua Bengio, & Aaron Courville
Synopsis: The foundational theoretical textbook covering broad concepts in deep learning, linear algebra, probability, neural network architecture, and deep generative models.
Spoiler:
17. The Hundred-Page Machine Learning Book
Author: Andriy Burkov
Synopsis: A compact overview covering core classical and modern machine learning algorithms, math foundations, and practical guidance in a brief format.
Spoiler:
18. Co-Intelligence: Living and Working with AI
Author: Ethan Mollick
Synopsis: Examines how generative AI impacts work, education, and human collaboration, offering practical strategies for effectively partnering with AI.
Spoiler:
19. Why Machines Learn: The Elegant Math Behind Modern AI
Author: Anil Ananthaswamy
Synopsis: Explores the mathematical concepts, historical milestones, and foundational ideas that power modern artificial intelligence and machine learning algorithms.
Spoiler:
20. The Alignment Problem: Machine Learning and Human Values
Author: Brian Christian
Synopsis: An investigation into how machine learning models learn human biases, default choices, and implicit assumptions, and how researchers are working to keep AI safe and aligned with human values.
Spoiler:
If you need help accessing these, please let me know. I can try to fish them out again and update this thread.