- ホーム
- > 洋書
- > 英文書
- > Internet / General
Full Description
STOP building fragile AI wrappers; START designing resilient AI systems.
Key Features
From LLM fundamentals to real-world practicalities
Patterns and principles for architecting LLM-based systems
Learn from in-depth case studies
Decouple from premium models using tiered fallback
Event-driven architectures for decoupling high-latency agentic workflows
Cost management approaches
Security strategies for LLM systems
Glossary of LLM and AI systems design terminology included
Book 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 learn
Architect a complete, production-grade AI-powered system from scratch
Design and mitigate the unique challenges of LLM APIs, like high latency and cost
Implement key software engineering patterns like circuit breakers and rate limiting for AI systems
Choose the right databases and data models for AI applications, including vector search engines
Build a scalable and resilient system that can handle high load and ensure user privacy
Who 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.
Contents
Table of Contents
Atomic Units of LLM Systems
Core Architectural Patterns for LLM System Design
Case Study: Designing AI-Native IDEs
Case Study: Adaptive Learning Platform
Case Study: AI-Powered Search for E-Commerce Platforms
Case Study: AI-Powered Customer Support Agent
Glossary
