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 multiple locations in the US or from the UK, 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.