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STOP building fragile AI wrappers; START designing resilient AI systems.Key FeaturesFrom LLM fundamentals to real-world practicalitiesPatterns and principles for architecting LLM-based systemsLearn from in-depth case studiesDecouple from premium models using tiered fallbackEvent-driven architectures for decoupling high-latency agentic workflowsCost management approachesSecurity strategies for LLM systemsGlossary of LLM and AI systems design terminology includedBook DescriptionMany companies are trying to turn their small AI experiments into big products, but they lack a good plan.Engineers need a practical guide to building these new AI systems the right way, so that they can handle scale, won't cost too much to build or operate, and perform reliably. This book is that guide, combining technical depth with breadth and practicality. Starting from LLM fundamentals, the book details the architectural patterns and design principles needed to build production-grade AI systems. In-depth case studies then show you how to apply them to a range of real-world application scenarios, including AI-native IDEs, adaptive learning platforms, and intelligent search solutions.The book provides a deep, practical look at the real-world challenges and solutions for building systems with LLMs at their core.What you will learnArchitect a complete, production-grade AI-powered system from scratchDesign and mitigate the unique challenges of LLM APIs, like high latency and costImplement key software engineering patterns like circuit breakers and rate limiting for AI systemsChoose the right databases and data models for AI applications, including vector search enginesBuild a scalable and resilient system that can handle high load and ensure user privacyWho this book is forThis book will be an invaluable learning resource for engineers, architects and leads working with LLMs or looking to integrate LLMs into their existing systems.
STOP building fragile AI wrappers; START designing resilient AI systems.Key FeaturesFrom LLM fundamentals to real-world practicalitiesPatterns and principles for architecting LLM-based systemsLearn from in-depth case studiesDecouple from premium models using tiered fallbackEvent-driven architectures for decoupling high-latency agentic workflowsCost management approachesSecurity strategies for LLM systemsGlossary of LLM and AI systems design terminology includedBook DescriptionMany companies are trying to turn their small AI experiments into big products, but they lack a good plan.Engineers need a practical guide to building these new AI systems the right way, so that they can handle scale, won't cost too much to build or operate, and perform reliably. This book is that guide, combining technical depth with breadth and practicality. Starting from LLM fundamentals, the book details the architectural patterns and design principles needed to build production-grade AI systems. In-depth case studies then show you how to apply them to a range of real-world application scenarios, including AI-native IDEs, adaptive learning platforms, and intelligent search solutions.The book provides a deep, practical look at the real-world challenges and solutions for building systems with LLMs at their core.What you will learnArchitect a complete, production-grade AI-powered system from scratchDesign and mitigate the unique challenges of LLM APIs, like high latency and costImplement key software engineering patterns like circuit breakers and rate limiting for AI systemsChoose the right databases and data models for AI applications, including vector search enginesBuild a scalable and resilient system that can handle high load and ensure user privacyWho this book is forThis book will be an invaluable learning resource for engineers, architects and leads working with LLMs or looking to integrate LLMs into their existing systems.
