AI NATIVE ARCHITECTING KNOWLEDGE - Analysing & Designing RAG Systems for AI-Native Software
Language: English
Published by Amazon Digital Services LLC - Kdp Jul 2026, 2026
- Softcover
- New
Seller: AHA-BUCH GmbH, Einbeck, GermanyAHA-BUCH GmbH
AbeBooks seller since August 14, 2006
Condition: New
£ 25.76
Quantity: 2 available
Add to basketItem description from seller
Neuware - Your LLM is brilliant - until you ask it about YOUR data.It hallucinates. Its knowledge is frozen in the past. It has never seen your company's documents. Retrieval-Augmented Generation (RAG) is the architecture that fixes all three - and this book is the map that takes you from your first chunk to a fully agentic system.Architecting Knowledge is not another collection of scattered tutorials. It is a complete engineering curriculum: 34 concise chapters, 7 parts, one natural progression - why RAG exists, how a pipeline is anatomised, which building blocks to master, how to compose them for production, and how to design a system of your own.Inside the map: - The anatomy of a RAG pipeline - ingestion, chunking, embeddings, vector databases, top-K retrieval and cited answers, explained step by step;- 14 foundational building blocks - from Naive RAG, Multi-Query and HyDE through Hybrid Search, Multimodal RAG, Graph RAG, RAPTOR, Reranking, CRAG, Self-RAG, up to Adaptive, Agentic and Modular RAG - with the strengths, weaknesses and cost of each;- 6 production-grade composite architectures - including 'The Gold Standard', 'The Self-Correcting Agent' and 'Semantic Routing' - plus a selection matrix and decision tree that match architecture to requirements, budget and SLA;- The full engineering discipline - schema design, evaluation suites, the economics of RAG, LLMOps, observability, security, governance, scaling and reliability;- 3 hands-on capstone projects - an internal wiki assistant, a market-research agent, and a vast-archive analyser - strong enough to anchor an AI-Native Engineer portfolio. Built to be taught - and to be practised.Every chapter opens with clear learning objectives, is illustrated with an architecture diagram, closes with a self-check quiz, and bridges naturally into the next. Each section is deliberately kept to roughly half a page: dense enough to be rigorous, short enough to stay readable.Who this book is for: - Software engineers moving into AI-Native application development;- Architects and tech leads choosing a RAG strategy for the enterprise;- Students and self-learners who want one coherent path instead of a hundred blog posts. Book 1 of the AI-Native Software Engineering series by MOBILUCK - code247.ai - fourteen blocks, six architectures, three projects, one method.…
Seller Inventory # 9798190038811
- Title
- AI NATIVE ARCHITECTING KNOWLEDGE - Analysing & Designing RAG Systems for AI-Native Software
- Author
- V& Mobiluck - Code247 Ai
- Publisher
- Amazon Digital Services LLC - Kdp Jul 2026
- Publication year
- 2026
- Condition
- Neu
- Binding
- Taschenbuch
- Language
- English
- ISBN 13
- 9798190038811
- Item weight
- 467 grams
- Dimensions
- 229x152x18 mm
- Series
- Book 5 of 5: The AI-Native Series
Your LLM is brilliant — until you ask it about YOUR data.
It hallucinates. Its knowledge is frozen in the past. It has never seen your company's documents. Retrieval-Augmented Generation (RAG) is the architecture that fixes all three — and this book is the map that takes you from your first chunk to a fully agentic system.
Architecting Knowledge is not another collection of scattered tutorials. It is a complete engineering curriculum: 34 concise chapters, 7 parts, one natural progression — why RAG exists, how a pipeline is anatomised, which building blocks to master, how to compose them for production, and how to design a system of your own.
Inside the map:
- The anatomy of a RAG pipeline — ingestion, chunking, embeddings, vector databases, top-K retrieval and cited answers, explained step by step;
- 14 foundational building blocks — from Naive RAG, Multi-Query and HyDE through Hybrid Search, Multimodal RAG, Graph RAG, RAPTOR, Reranking, CRAG, Self-RAG, up to Adaptive, Agentic and Modular RAG — with the strengths, weaknesses and cost of each;
- 6 production-grade composite architectures — including "The Gold Standard", "The Self-Correcting Agent" and "Semantic Routing" — plus a selection matrix and decision tree that match architecture to requirements, budget and SLA;
- The full engineering discipline — schema design, evaluation suites, the economics of RAG, LLMOps, observability, security, governance, scaling and reliability;
- 3 hands-on capstone projects — an internal wiki assistant, a market-research agent, and a vast-archive analyser — strong enough to anchor an AI-Native Engineer portfolio.
Built to be taught — and to be practised.
Every chapter opens with clear learning objectives, is illustrated with an architecture diagram, closes with a self-check quiz, and bridges naturally into the next. Each section is deliberately kept to roughly half a page: dense enough to be rigorous, short enough to stay readable.
Who this book is for:
- Software engineers moving into AI-Native application development;
- Architects and tech leads choosing a RAG strategy for the enterprise;
- Students and self-learners who want one coherent path instead of a hundred blog posts.
Book 1 of the AI-Native Software Engineering series by MOBILUCK · code247.ai — fourteen blocks, six architectures, three projects, one method.
"Synopsis" may belong to another edition of this title.
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