Native Architecting Knowledge Analysing by Mobiluck Code247 Ai (4 results)

PAP. Condition: New. New Book. Shipped from UK. Established seller since 2000.

Taschenbuch. Condition: Neu. 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.…

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Paperback. Condition: new. Paperback. 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. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.…