Open Daily: 10am - 10pm | Alley door pickup 10am - 7pm 3038 Hennepin Ave Minneapolis, MN
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  • Open Daily: 10am - 10pm
    Alley-side Pickup: 10am - 7pm

    3038 Hennepin Ave Minneapolis, MN
    612-822-4611

Open Daily: 10am - 10pm | Alley-side Pickup: 10am - 7pm
3038 Hennepin Ave Minneapolis, MN
612-822-4611
RAG Demystified: What It Is, Why It Matters, and How to Build It

RAG Demystified: What It Is, Why It Matters, and How to Build It

Constantin, Alex

Paperback

Business GeneralProgramming

ISBN13: 9798195963439
Publisher: Independently Published
Published: May 7 2026
Pages: 282
Weight: 0.84
Height: 0.59 Width: 6.00 Depth: 9.00
Language: English
Large language models are brilliant - until they need to know something they were never trained on. Ask them about your company's documents, last week's news, your customer's account, or the latest API spec, and the cracks appear: confident answers built on nothing, made-up citations, and silent failures that erode trust. This is the knowledge problem at the heart of modern AI, and it is the reason RAG exists.

Retrieval-Augmented Generation is the technique that fixes the gap. Instead of hoping an LLM remembers, RAG lets it look things up - in your docs, your database, your knowledge base - and answer with grounded, current, verifiable information. It has quietly become the backbone of serious AI systems in production, from internal copilots to customer-facing assistants. If you build with LLMs, RAG is no longer optional.

This book is your complete, practical guide to RAG, from first principles to production. You will learn how the full pipeline works - chunking, embeddings, vector databases, retrieval strategies, reranking, and generation - and how the pieces fit together into systems that actually perform. You will see why naive RAG breaks, what advanced patterns like hybrid search, query rewriting, and agentic retrieval solve, and how to evaluate, monitor, and harden a RAG system for real users. Safety, ethics, cost, and the hard trade-offs of shipping AI are covered too.

Master the foundations: why LLMs hallucinate, how retrieval grounds them, and what makes RAG different from fine-tuning.

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