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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