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Brief Summary of Book: Agentic RAG Systems: Design, Build and u0026 Deploy Production Ready Knowledge-Powered AI Agents: Master GraphRAG, Vector Search, LangGraph, and Self-Correcting Retrieval for Scalable Real-World Applications by Raymond Norman
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40% of production RAG pipelines fail at retrieval. Yours doesn’t have to. The passage highlights how fragile RAG systems are in production. Without strong retrieval strategies, observability, and cost management, they risk hallucinations, degraded performance, financial surprises, and serious domain-specific errors. These are not hypothetical failures. They are the production incidents that frame every chapter of this book. Retrieval-Augmented Generation is one of the most consequential ideas in applied Artificial Intelligence — and one of the most misunderstood. In the three years since RAG entered mainstream engineering practice, it has been treated, by turn, as a quick fix for hallucination, a magic substitute for fine-tuning, and a static pattern that you implement once and forget. None of these framings have survived contact with production.This book is written for AI engineers, data scientists, machine learning engineers, and software architects who are building production retrieval-augmented systems in 2026 and beyond. It assumes you have already encountered RAG at least once — perhaps you built a naive pipeline that worked in development but failed in production, or you have a working system that hallucinates more than you can tolerate. You are now ready to engineer retrieval at the level the technology actually demands. This book assumes comfort with Python, basic familiarity with language models and prompting, and some prior exposure to retrieval concepts at the level of having built or read about a naive RAG pipeline. You do not need a background in information retrieval theory. You do not need to be an ML researcher. You do need to be willing to think carefully about production failure modes — because that, more than any specific technique, is the discipline this book is trying to teach. Code examples are provided throughout in Python, using framework-neutral patterns wherever possible. Where a specific framework illustrates a concept best, the example is shown in LangGraph, LlamaIndex, or pure Python with explicit notes on how to translate the pattern to other frameworks.
Overview – Agentic RAG Systems: Design, Build and u0026 Deploy Production Ready Knowledge-Powered AI Agents: Master GraphRAG, Vector Search, LangGraph, and Self-Correcting Retrieval for Scalable Real-World Applications by Raymond Norman
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- Full Book Name: Agentic RAG Systems: Design, Build and u0026 Deploy Production Ready Knowledge-Powered AI Agents: Master GraphRAG, Vector Search, LangGraph, and Self-Correcting Retrieval for Scalable Real-World Applications
- Author Name: Raymond Norman
- Book Genre: Non-Fiction, Tech BOOKGENRE Devices
- Series Synopsis: “”
- ISBN # 9798184336916
- ASIN # B0H6NF9Y51
- Edition Language: English
- Date of Publication: June 25, 2026
- File Name: Agentic_RAG_Systems_Design_Build_n_Deploy_-_Raymond_Norman.epub, Agentic_RAG_Systems_Design_Build_n_Deploy_-_Raymond_Norman.pdf
- EPUB Size: 1.9 MB
- PDF Size: 2.6 MB
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