Prompt engineering by Quick component: Patterns and pitfalls | Amazon Web Services
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 for market analysis, Amazon Quick Flows for automation, Amazon Quick Sight for data visualization, chat agents for team knowledge access, or action integrations for cross-system workflows. Whatever your goal, the following techniques will help you move from generic outputs to precise, actionable results.
Quick Research
Getting useful output from Amazon Quick Research depends on how you frame the research objective. The agent takes your objective, breaks it into sub-topics, searches across enterprise data and external sources, then delivers a structured report with citations. A vague...
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