{
"item_title" : "AI Engineering In Practice",
"item_author" : [" Noa Stride "],
"item_description" : "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.",
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Overview
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 Non-Returnable
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Details
- ISBN-13: 9798286260560
- ISBN-10: 9798286260560
- Publisher: Independently Published
- Publish Date: June 2025
- Dimensions: 11 x 8.5 x 0.34 inches
- Shipping Weight: 0.84 pounds
- Page Count: 158
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