AI NATIVE ARCHITECTING KNOWLEDGE - Analysing & Designing RAG Systems for AI-Native Software

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.