AI NATIVE ARCHITECTING KNOWLEDGE - Analysing & Designing RAG Systems for AI-Native Software (The AI-Native Series)

MOBILUCK - CODE247.AI, V? TRÍ CÔNG

ISBN 13: 9798190038811

Editore: Independently published, 2026

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Codice articolo I-9798190038811

Dati bibliografici

• Titolo: AI NATIVE ARCHITECTING KNOWLEDGE - Analysing & Designing RAG Systems for AI-Native Software (The AI-Native Series)
• Editore: Independently published
• Data di pubblicazione: 2026
• Lingua: Inglese
• ISBN 13: 9798190038811
• Rilegatura: Brossura
• Condizione: New

Descrizione articolo

Riassunto

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.