The Seminal Papers
Principles for architecting reliable and verifiable AI. A curated deep-dive into the foundational research papers that shaped RAG, with practical commentary and implementation guidance.
Retrieval Augmented Generation (RAG) grounds LLM prompts in your own content rather than relying only on a model's training data. RAG has grown from a simple prompt-engineering workflow into a set of data analysis, storage, and retrieval techniques. Retrieval Augmented Generation, The Seminal Papers explores the foundational research papers that explain why RAG works, how it's built, and what sets it apart.
The book traces RAG's evolution from the breakthroughs of REALM, naive RAG, and DPR to advanced architectures like Fusion-in-Decoder and Atlas. Over 40 code samples, architecture diagrams, and industry case studies make each concept concrete. As you master the patterns behind RAG, you'll understand the tradeoffs, diagnose failures, and evaluate and improve your own implementations.
For ML engineers, data scientists, and software developers comfortable with Python and the basics of deep learning. No advanced math is required.