Amazon Quick 各组件提示工程模式与陷阱
原标题：Prompt engineering by Quick component: Patterns and pitfalls
AI 摘要
AWS Machine Learning Blog 发布系列文章第二部分，逐组件讲解 Amazon Quick 的提示工程模式与常见陷阱。文章分别针对 Quick Research、Quick Flows 和 Quick Sight 给出具体写法建议，例如研究目标需明确目的、受众与范围，工作流提示需写清时间、数据源与输出格式，分析查询需包含业务问题、指标、维度、时间范围与可视化类型。核心影响是帮助用户从泛化输出转向精确、可执行的结果。
正文节选
Prompt engineering by Quick component: Patterns and pitfalls In Part 1 of this series, we covered the foundational principles of prompt engineering in Amazon Quick: specificity, context-setting, few-shot examples, and the CRISPE framework for complex requests. Those principles apply universally. In this post, we go component by component, showing you how each Quick capability interprets prompts differently and what patterns get the best results from each one. You might use Amazon Quick Research