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官方來源· AWS Artificial Intelligence· AI· 發布於 2026年9月29日
AWS 分享 Amazon Quick 提示工程基礎，聚焦四種提示方法
AWS 於 2026 年 9 月 29 日發布兩篇系列文章的第一篇，介紹如何透過明確指示、提供脈絡、少量示例及 CRISPE 架構，提升 Amazon Quick AI 功能回應的一致性與品質。
完整報導
AWS 於 2026 年 9 月 29 日發布〈Prompt engineering fundamentals for Amazon Quick〉，作為提示工程兩篇系列文章的第一篇。文章聚焦於提示如何影響 Amazon Quick 的 AI 功能回應準確度，並整理可重複使用的基礎方法。
AWS 列出的方法包括讓指示更具體、設定任務脈絡、加入少量示例，以及使用 CRISPE 架構。這些內容旨在協助使用者更清楚地表達需求，並讓 Amazon Quick 在不同請求中產生較一致、較高品質的結果。
這篇文章把提示撰寫視為使用 Amazon Quick 的實務環節，而非只介紹 AI 功能本身。AWS 將內容安排為兩篇系列的首篇；其後續篇章可望延續這項主題，但此次公告未提供第二篇的發布日期或內容細節。Amazon Quick 所屬的產品脈絡也在 AWS 先前介紹中有所呈現：AWS 曾將 Amazon Quick Suite 描述為能回答問題並採取行動的 agentic teammate。
為什麼值得注意
對使用者而言，回應品質不只取決於選用哪項 AI 功能，也與如何描述任務有關。AWS 這篇指南將具體指示、背景脈絡與示例等方法整理成可套用的做法，讓 Amazon Quick 的工作流程更容易獲得一致回應。
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 fundamentals for Amazon Quick
Daiquan Nkere
“Prompt engineering in Amazon Quick shapes how accurately its AI-powered features respond to your requests. Part 1 of a two-part series covers the foundational principles and reusable frameworks (specificity, context-setting, few-shot examples, and the CRISPE framework) for consistent, high-quality results across Amazon Quick.”
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