Most developers can spin up a RAG pipeline in an afternoon using LangChain or LlamaIndex. Far fewer understand why retrieval fails or how to fix it. This book is for those who want to go deeper.
'RAG From First Principles' dismantles the retrieval-augmented generation stack layer by layer, how documents are ingested and parsed, why chunking strategy directly impacts answer quality, how embedding models encode meaning, what happens inside a vector database, and how sparse and dense retrieval interact in a hybrid system. Written by Jia Huang, a research engineer and bestselling AI author, it brings research depth and production experience to one of AI's most critical engineering disciplines.
Structured as a progressive dialogue between a seasoned engineer and two students, the book surfaces the questions practitioners actually ask. Each chapter builds on the last, from data import and chunking through embedding selection, index design, hybrid search, and post-retrieval processing, into response generation, evaluation, and advanced paradigms including GraphRAG, Agentic RAG, and Modular RAG.
By the end, you'll have the architectural understanding to optimize, debug, and extend your RAG systems with confidence.

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Spis treści

RAG from First Principles. Engineering retrieval-augmented generation systems with Python, LangChain, and LlamaIndex eBook -- spis treści

• 1. Data Import
• 2. Text Chunking
• 3. Information Embedding
• 4. Vector Storage
• 5. Pre-Retrieval Processing
• 6. Index Optimization
• 7. Index Optimization
• 8. Response Generation
• 9. System Evaluation
• 10. Complex RAG Paradigms