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AWS 發布 Amazon Quick 提示工程第二篇，拆解五類元件的提示技巧
AWS 9 月 29 日發布系列第二篇文章，分別整理 Quick Research、Quick Flows、Quick Sight、聊天代理與動作整合的提示寫法及常見錯誤。
完整報導
AWS 於 2026 年 9 月 29 日發布 Amazon Quick 提示工程系列第二篇，從各個元件的使用方式切入，說明如何撰寫提示，以及哪些常見疏漏可能讓結果變得含糊或不完整。文章涵蓋 Quick Research、Quick Flows、Quick Sight、聊天代理與動作整合。
針對 Quick Research，AWS 建議提示交代研究目標、讀者和範圍，並將複雜題目拆成具體問題；對 Quick Flows，則應明確描述觸發條件、執行步驟與錯誤處理。使用 Quick Sight 查詢時，應說清楚商業問題、指標、分類維度、時間範圍及希望呈現的圖表，避免讓系統自行猜測分析方式。
文章也建議為聊天代理界定角色、專業範圍與可用知識來源，並說明遇到未知問題時應如何回應；使用動作整合時，提示應提供必要參數、交代各步驟的依賴關係，若涉及大量或具破壞性的操作，先安排審核再執行。AWS 列出的通用陷阱包括指令模糊、一次塞入過多要求、缺少必要脈絡或輸出格式，以及未用實際情境測試。
這是系列第一篇之後的元件篇。第一篇介紹明確性、提供脈絡、少量示例與 CRISPE 架構；第二篇則將這些基礎原則延伸到不同 Quick 功能，指出提示內容應配合工作類型調整，而非只靠一套通用寫法。
為什麼值得注意
這篇指南把提示工程落到五種具體工作流程：研究、流程自動化、資料分析、知識型聊天代理及跨系統操作。對使用者而言，清楚界定輸入、範圍與審核步驟，可減少系統猜測，也能讓自動化或外部操作更容易檢查。
來源證據
先看主要證據，再查看佐證來源與來源脈絡。
主要證據
1Prompt engineering by Quick component: Patterns and pitfalls
Daiquan Nkere
“Part 2 of our Amazon Quick prompt engineering series goes component by component. Learn the prompt patterns that get the best results from Amazon Quick Research, Quick Flows, Quick Sight, chat agents, and action integrations, plus the common pitfalls to avoid.”
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