Amazon Quick 提示工程基础：通用原则与结构化框架
原标题：Prompt engineering fundamentals for Amazon Quick
AI 摘要
AWS Machine Learning Blog 发布了一篇关于 Amazon Quick 提示工程基础的文章，介绍如何通过清晰具体、提供上下文和示例来提升该平台 AI 功能的响应质量。文章提出了 CRISPE 通用提示框架，并预告了针对 Research、Flows、Sight、Chat Agents 和 Action Integrations 等组件的专用框架（如 RADAR、ARCHITECT、QUEST）。内容属于面向企业用户的方法论教程，强调可复用提示模式在团队内的规模化价值。
正文节选
Prompt engineering fundamentals for Amazon Quick Prompt engineering in Amazon Quick determines how accurately and reliably the platform’s AI-powered features respond to your natural-language requests. Whether you’re building custom agents, authoring automation flows, or querying data through conversational analytics, the way you structure your prompts directly shapes the quality of the output you receive. In this post, you will learn the foundational principles and structured frameworks that pro