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

By VU TRI CONG MOBILUCK - CODE247.AI

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.

• Format: Paperback
• Language: English
• ISBN: B0HCZXTJTS
• ISBN13: 9798190038811
• Release Date: July 2026
• Publisher: Independently published
• Length: 347 Pages
• Weight: 1.03 lbs.
• Dimensions: 9.0" x 0.8" x 6.0"