A rigorous, code-first guide to RAG engineering by a bestselling AI author. Master data ingestion, chunking, embeddings, vector storage, hybrid retrieval, reranking, and evaluation from the ground up.
Free with your book: DRM-free PDF version + access to Packt's next-gen Reader*
Key Features:
- Engineer RAG systems layer by layer, from ingestion to evaluation
- Master hybrid retrieval, reranking, and index optimization strategies
- Learn through a dialogue-driven, code-first teaching style used by 10,000+ of students
Book Description:
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, explaining 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 both 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, covering topics from data import and chunking to embedding selection, index design, hybrid search, and post-retrieval processing, before moving on to 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.
*Email sign-up and proof of purchase required
What You Will Learn:
- Parse and ingest diverse document types like PDFs, tables, images, web pages, and structured data
- Apply the right chunking strategy for your content type and retrieval goals
- Select, compare, and fine-tune embedding models for your domain
- Design vector indexes and choose the right similarity metrics for production use
- Improve result quality with reranking methods including RRF, cross-encoders, and ColBERT
- Integrate retrieval results into generation pipelines using prompt engineering and Self-RAG
Who this book is for:
This book is for AI engineers, ML practitioners, and software developers building LLM-powered applications who want a deeper understanding of how retrieval actually works, not just how to call a framework. It is ideal for readers who have built a basic RAG pipeline and now want architectural clarity, optimization strategies.
Technical leads and architects designing production AI systems will find its systematic treatment of indexing, hybrid search, reranking, and evaluation particularly valuable. Familiarity with Python and foundational LLM concepts is assumed.
Table of Contents
- Data Import
- Text Chunking
- Information Embedding
- Vector Storage
- Pre-Retrieval Processing
- Index Optimization
- Retrieval Post-Processing
- Response Generation
- System Evaluation
- Complex RAG Paradigms
外文書商品之書封，為出版社提供之樣本。實際出貨商品，以出版社所提供之現有版本為主。部份書籍，因出版社供應狀況特殊，匯率將依實際狀況做調整。
無庫存之商品，在您完成訂單程序之後，將以空運的方式為你下單調貨。為了縮短等待的時間，建議您將外文書與其他商品分開下單，以獲得最快的取貨速度，平均調貨時間為1~2個月。
為了保護您的權益，「三民網路書店」提供會員七日商品鑑賞期(收到商品為起始日)。
若要辦理退貨，請在商品鑑賞期內寄回，且商品必須是全新狀態與完整包裝(商品、附件、發票、隨貨贈品等)否則恕不接受退貨。
