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使用人工智慧的應用程式開發模式(繁體中文版)

透過這本開創性的人工智慧應用程式架構專書,釋放AI在您應用程式中的強大潛力。探索實用的模式與原則,學習如何建構智慧型、具適應性且以使用者為中心的軟體系統,充分運用大型語言模型和人工智慧元件的優勢。

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

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About

About the Book

《使用人工智慧的應用程式開發模式》是一本開創性的著作,探討人工智慧(AI)與應用程式開發的交會點。在本書中,AI驅動的顧問平台 Olympia 的共同創辦人、知名軟體開發者 Obie Fernandez 分享了他在建構AI驅動應用程式一年歷程中的寶貴見解與經驗。

聆聽本書的語音概述

透過引人入勝的敘事章節與實用的模式參考相結合,Obie 提供了一份全面的指南,闡述如何在應用程式開發中運用大型語言模型(LLMs)的力量。他介紹了創新的模式,如「多工作者模式」、「自我修復數據」和「上下文內容生成」,這些模式使開發者能夠建構智能、適應性強且以用戶為中心的應用程式。

有別於其他著重於理論概念或深入探討機器學習算法的AI書籍,本書採用務實的方法。它提供具體的範例、真實世界的使用案例,以及如何將AI組件和功能整合到應用程式架構中的可行建議。Obie 分享了他的成功經驗、面臨的挑戰和學習到的教訓,為AI在軟體開發中的實際應用提供了獨特的視角。

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

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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 序言
    • 前言
      • 關於本書
      • 關於程式碼範例
      • 我不會涵蓋的內容
      • 這本書適合誰
      • 建立共同詞彙
      • 參與方式
      • 致謝
      • 插圖是怎麼回事?
      • 關於精實出版
      • 關於作者
    • 簡介
      • 關於軟體架構的思考
      • 什麼是大型語言模型?
      • 理解推理
      • 思考效能表現
      • 實驗不同的 LLM 模型
      • 複合式人工智能系統
    第一部分:基本方法與技術
    • 收窄路徑
      • 潛在空間:難以理解的浩瀚
      • 如何“縮小“路徑
      • 原始模型與指令調校模型的比較
      • 提示詞工程
      • 提示詞提煉
      • 那麼微調呢?
    • 檢索增強生成 (RAG)
      • 什麼是檢索增強生成?
      • RAG如何工作?
      • 為什麼要在您的應用程序中使用RAG?
      • 在您的應用程序中實施RAG
      • 命題分塊
      • RAG的實際應用案例
      • 智慧查詢優化(IQO)
      • 重新排序
      • RAG評估(RAGAs)
      • 挑戰與未來展望
    • 眾多工作者
      • AI 工作者作為獨立可重用組件
      • 帳戶管理
      • 電子商務應用
      • 醫療保健應用
      • AI 工作器作為流程管理器
      • 將 AI 工作者整合到您的應用程式架構中
      • AI 工作者的可組合性和編排
      • 將傳統 NLP 與 LLM 結合
    • 工具使用
      • 什麼是工具使用?
      • 工具使用的潛力
      • 工具使用工作流程
      • 工具使用的最佳實踐
      • 組合和鏈接工具
      • 未來方向
    • 串流處理
      • 實作 ReplyStream
      • 「對話循環」
      • 自動續接
      • 結論
    • 自我修復數據
      • 實際案例研究:修復損壞的JSON
      • 考量因素與禁忌症
    • 情境內容生成
      • 個人化
      • 生產力
      • 快速迭代和實驗
      • AI 驅動的本地化
      • 使用者測試與回饋的重要性
    • 生成式使用者介面
      • 為使用者介面生成文案
      • 定義生成式使用者介面
      • 範例
      • 轉向結果導向設計
      • 挑戰與考量
      • 未來展望與機會
    • 智能工作流程編排
      • 業務需求
      • 主要優勢
      • 關鍵模式
      • 異常處理和恢復
      • 在實踐中實施智能工作流程編排
      • 監控與日誌記錄
      • 可擴展性和效能考量
      • 工作流程的測試和驗證
    第二部分:模式
    • 提示工程
      • 思維鏈
      • 模式切換
      • 角色分配
      • 提示物件
      • 提示詞模板
      • 結構化輸入輸出
      • 提示鏈接
      • 提示重寫器
      • 回應圍欄
      • 查詢分析器
      • 查詢重寫器
      • 腹語術師模式
    • 離散元件
      • 謂詞
      • API 外觀模式
      • 結果解譯器
      • 虛擬機器
      • 規格與測試
    • 人機協作系統 (HITL)
      • 高層次模式
      • 升級處理
      • 反饋循環
      • 被動資訊輻射
      • 協作決策制定 (CDM)
      • 持續學習
      • 倫理考量
      • 技術進步與未來展望
    • 智慧錯誤處理
      • 傳統錯誤處理方法
      • 情境錯誤診斷
      • 智慧型錯誤報告
      • 預測性錯誤預防
      • 智能錯誤恢復
      • 個人化錯誤通訊
      • 自適應錯誤處理工作流程
    • 品質控制
      • 評估器
      • 護欄
      • 防護機制和評估:一體兩面
    詞彙表
      • 詞彙表

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