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Patterns of Application Development Using AI (简体中文版)

通过这本开创性的人工智能驱动应用架构书籍,解锁AI在应用程序中的强大潜能。探索实用的模式和原则,学习如何构建智能化、自适应且以用户为中心的软件系统,充分发挥大语言模型和人工智能组件的潜力。

This book is a translation into Chinese (Simplified) of Patterns of Application Development Using AI which was originally written in English

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About

About the Book

《使用人工智能的应用程序开发模式》是一本开创性的著作,探讨了人工智能(AI)与应用程序开发的交叉领域。在本书中,AI驱动的咨询平台 Olympia 的联合创始人、著名软件开发者Obie Fernandez分享了他在构建AI驱动应用程序一年历程中获得的宝贵见解和经验。

收听本书的音频概述

通过将叙述性章节与实用模式参考相结合的引人入胜方式,Obie提供了一份全面的指南,讲解如何在应用程序开发中利用大语言模型(LLMs)的力量。他介绍了创新性的模式,如"多工作者模式"、"自愈数据"和"上下文内容生成",这些模式使开发者能够构建智能、适应性强且以用户为中心的应用程序。

与其他专注于理论概念或深入机器学习算法细节的AI书籍不同,本书采用了务实的方法。它提供了具体的示例、真实世界的使用案例,以及关于如何将AI组件和功能整合到应用程序架构中的可行建议。Obie分享了他的成功经验、遇到的挑战和学到的经验教训,为AI在软件开发中的实际应用提供了独特的视角。

This book is a translation into Chinese (Simplified) of Patterns of Application Development Using AI which was originally written in English

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About the Authors

Obie Fernandez

The "one and only" Obie Fernandez is an avid writer and technology enthusiast, in addition to achieving worldwide success as an electronic music producer and touring DJ. He is a Principal Engineer at Shopify and boasts a legendary 30 year career in software development and entrepreneurship.

Obie has been CTO and co-founder of many startups including Mark Zuckerberg's beloved Andela and Trevor Owen's Lean Startup Machine. His published books include Patterns of Application Development Using AI many editions of The Rails Way and the acclaimed business title The Lean Enterprise. He also founded one of the world's best known Ruby on Rails web design and development agencies, Hashrocket and served for many years as the series editor for Addison-Wesley's Professional Ruby Series.

On the rare occasion when Obie is not busy building products, consulting clients or writing books, you can find him behind the lens of his camera or DJing in the dust at Burning Man.

Follow @obie on Twitter or email him at obiefernandez@gmail.com 

Leanpub Podcast

Episode 24

An Interview with Obie Fernandez

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Contents

Table of Contents

    • Gregor Hohpe的前言
    • 序言
      • 关于本书
      • 关于代码示例
      • 本书不涵盖的内容
      • 本书适合谁
      • 建立共同词汇
      • 参与其中
      • 致谢
      • 插图是怎么回事?
      • 关于精益出版
      • 关于作者
    • 引言
      • 关于软件架构的思考
      • 什么是大语言模型?
      • 理解推理
      • 性能考量
      • 尝试不同的大语言模型
      • 复合人工智能系统
    第一部分:基本方法与技术
    • 缩窄路径
      • 潜在空间:难以理解的浩瀚
      • 如何“缩小“路径
      • 原始模型与指令调优模型的对比
      • 提示工程
      • 提示精炼
      • 那么微调呢?
    • 检索增强生成(RAG)
      • 什么是检索增强生成?
      • RAG是如何工作的?
      • 为什么要在应用程序中使用RAG?
      • 在应用程序中实现RAG
      • 命题分块
      • RAG的真实应用案例
      • 智能查询优化(IQO)
      • 重排序
      • RAG评估(RAGAs)
      • 挑战与未来展望
    • 众多工作器
      • 作为独立可重用组件的AI工作器
      • 账户管理
      • 电子商务应用
      • 医疗保健应用
      • AI工作器作为流程管理器
      • 将AI工作器集成到您的应用程序架构中
      • AI工作程序的可组合性和编排
      • 将传统NLP与LLM相结合
    • 工具使用
      • 什么是工具使用?
      • 工具使用的潜力
      • 工具使用工作流程
      • 工具使用的最佳实践
      • 组合和链接工具
      • 未来方向
    • 流处理
      • 实现ReplyStream
      • “对话循环”
      • 自动继续
      • 结论
    • 自修复数据
      • 实践案例研究:修复损坏的JSON
      • 考虑因素和禁忌症
    • 上下文内容生成
      • 个性化
      • 生产力
      • 快速迭代和实验
      • AI驱动的本地化
      • 用户测试和反馈的重要性
    • 生成式用户界面
      • 为用户界面生成文案
      • 定义生成式UI
      • 示例
      • 向成果导向设计的转变
      • 挑战和考虑因素
      • 未来展望与机遇
    • 智能工作流编排
      • 业务需求
      • 主要优势
      • 关键模式
      • 异常处理和恢复
      • 在实践中实施智能工作流编排
      • 监控和日志记录
      • 可扩展性和性能考虑因素
      • 工作流的测试和验证
    第二部分:模式
    • 提示工程
      • 思维链
      • 模式切换
      • 角色分配
      • 提示对象
      • 提示模板
      • 结构化输入输出
      • 提示链接
      • 提示重写器
      • 响应围栏
      • 查询分析器
      • 查询重写器
      • 腹语术师模式
    • 离散组件
      • 谓词
      • API外观模式
      • 结果解释器
      • 虚拟机
      • 规格说明和测试
    • 人在环路中(HITL)
      • 高层次模式
      • 升级处理机制
      • 反馈循环
      • 被动信息辐射
      • 协作决策(CDM)
      • 持续学习
      • 伦理考虑
      • 技术进步与未来展望
    • 智能错误处理
      • 传统错误处理方法
      • 上下文错误诊断
      • 智能错误报告
      • 预测性错误预防
      • 智能错误恢复
      • 个性化错误通信
      • 自适应错误处理工作流
    • 质量控制
      • 评估器
      • 防护机制
      • 护栏和评估:一枚硬币的两面
    术语表
      • 术语表

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