Publisher's 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.
