AI Engineering In Practice (Paperback)

Noa Stride

ISBN 13: 9798286260560

Published by Independently Published, 2025

About this Item

Paperback. Build Smarter AI Systems--from Blueprint to Production AI Engineering in Practice is your hands-on guide to designing, building, and deploying intelligent systems using Multi-Agent Architectures, Retrieval-Augmented Generation (RAG), the Model Context Protocol (MCP), and Large Language Models (LLMs). Whether you're a machine learning engineer, AI product lead, or aspiring technical founder, this book gives you practical tools, proven design patterns, and expert insights to go beyond theory and deliver real-world AI solutions. What You'll Learn: - How to build and orchestrate multi-agent systems with context-aware collaboration- Step-by-step RAG workflows using external knowledge sources- Techniques for effective prompt engineering and managing LLM output- Applying MCP to structure intelligent communication between agents- Real-world case studies from enterprise AI deployments This Book Is For: - AI engineers and software developers- Data scientists transitioning into AI productization- Technical PMs and architects building scalable LLM-powered applications Includes diagrams, code templates, performance metrics, and best practices. No fluff--just actionable AI engineering knowledge. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.

Seller Inventory # 9798286260560

Bibliographic Details

• Title: AI Engineering In Practice (Paperback)
• Publisher: Independently Published
• Publication Date: 2025
• Language: English
• ISBN 13: 9798286260560
• Binding: Paperback
• Condition: new

About this title

Synopsis

Build Smarter AI Systems—from Blueprint to Production

AI Engineering in Practice is your hands-on guide to designing, building, and deploying intelligent systems using Multi-Agent Architectures, Retrieval-Augmented Generation (RAG), the Model Context Protocol (MCP), and Large Language Models (LLMs).

Whether you're a machine learning engineer, AI product lead, or aspiring technical founder, this book gives you practical tools, proven design patterns, and expert insights to go beyond theory and deliver real-world AI solutions.

What You’ll Learn:

• How to build and orchestrate multi-agent systems with context-aware collaboration
• Step-by-step RAG workflows using external knowledge sources
• Techniques for effective prompt engineering and managing LLM output
• Applying MCP to structure intelligent communication between agents
• Real-world case studies from enterprise AI deployments

This Book Is For:

• AI engineers and software developers
• Data scientists transitioning into AI productization
• Technical PMs and architects building scalable LLM-powered applications

Includes diagrams, code templates, performance metrics, and best practices. No fluff—just actionable AI engineering knowledge.