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RAG from First Principles. Engineering retrieval-augmented generation systems with Python, LangChain, and LlamaIndex Jia Huang

(ebook) (audiobook) (audiobook) Język publikacji: angielski
RAG from First Principles. Engineering retrieval-augmented generation systems with Python, LangChain, and LlamaIndex Jia Huang - okladka książki

RAG from First Principles. Engineering retrieval-augmented generation systems with Python, LangChain, and LlamaIndex Jia Huang - okladka książki

RAG from First Principles. Engineering retrieval-augmented generation systems with Python, LangChain, and LlamaIndex Jia Huang - audiobook MP3

RAG from First Principles. Engineering retrieval-augmented generation systems with Python, LangChain, and LlamaIndex Jia Huang - audiobook CD

Autor:
Jia Huang
Ocena:
Bądź pierwszym, który oceni tę książkę
Stron:
300
Dostępne formaty:
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Do przechowalni

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.

O autorze książki

Jia Huang is a Lead Research Engineer at A*STAR (Agency for Science, Technology and Research), Singapore, where his work focuses on NLP, large language models, and applied AI engineering. With over twenty years of experience leading large-scale AI and data projects across government, finance, healthcare, and e-commerce, he brings an unusually practical lens to technically rigorous subjects. In recent years, his research has primarily focused on NLP pre-trained large models and FinTech applications.
He is the author of six bestselling technical books, including Hands-on AI Agent Development for Large Model Applications selected as one of JD Best Books of 2024 and GPT: How Large Models Are Built, named CSDN's Most Influential IT Book of 2023. His online RAG engineering course has been completed by over 10,000 students.

Packt Publishing - inne książki

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