Recommended Books

A curated collection of essential books for Retrieval Augmented Generation (RAG), Machine Learning, Deep Learning, and Artificial Intelligence.

Retrieval Augmented Generation (RAG)

• Building LLMs for Production
• Louis-François Bouchard, Louie Peters (2024). End-to-end LLM engineering with substantial coverage of retrieval pipelines, chunking strategies, and RAG evaluation in production settings.
• RAG Made Simple: The Complete Visual Guide to Retrieval-Augmented Generation
• Nir Diamant (2025). A visual, code-free walkthrough of 22 RAG techniques using diagrams and analogies, covering retrieval, chunking, reranking, and evaluation for engineers and product teams new to RAG.
• RAG-Driven Generative AI
• Denis Rothman (2024). Hands-on construction of RAG systems with vector stores, knowledge graphs, and LlamaIndex, focused on practical implementation patterns.
• Unlocking Data with Generative AI and RAG
• Keith Bourne (2024). Building retrieval-augmented systems with attention to vector stores, evaluation, governance, and security — written from a production-readiness lens.

Deep Learning (DL) & NLP

• Deep Learning
• Ian Goodfellow, Yoshua Bengio, Aaron Courville (2016). The definitive textbook on deep learning, covering foundational concepts and architectures.
• Deep Learning with Python
• François Chollet (2021). Practical guide to deep learning with Keras, written by the creator of Keras.
• Hands-On Large Language Models
• Jay Alammar, Maarten Grootendorst (2024). A visual and practical guide to understanding and using LLMs effectively.
• Natural Language Processing with Transformers
• Lewis Tunstall, Leandro von Werra, Thomas Wolf (2022). Build, train, and deploy state-of-the-art NLP models using the Hugging Face ecosystem.

Artificial Intelligence (AI) & Agents

• Artificial Intelligence: A Modern Approach
• Stuart Russell, Peter Norvig (2020). The standard university textbook on AI, covering a vast array of topics including agents and learning.
• The LLM Engineering Handbook
• Paul Iusztin, Maxime Labonne (2024). A practical guide for building, deploying, and scaling large language model-based applications.

Machine Learning (ML) & Data Science

• Designing Machine Learning Systems
• Chip Huyen (2022). An iterative process for production-ready applications, critical for real-world ML engineering.
• Machine Learning Yearning
• Andrew Ng (2018). Technical strategy for AI engineers, focusing on how to structure ML projects.
• Pattern Recognition and Machine Learning
• Christopher Bishop (2006). A comprehensive introduction to the fields of pattern recognition and machine learning.
• The Elements of Statistical Learning
• Trevor Hastie, Robert Tibshirani, Jerome Friedman (2009). A classic reference for statistical learning, data mining, and prediction.