Prompt engineering by Quick component: Patterns and pitfalls

Quick component: Patterns and pitfalls in prompt engineering

The article discusses the nuances of prompt engineering when working with Amazon Quick components such as Amazon Quick Research, Amazon Quick Flows, Amazon Quick Sight, chat agents, and action integrations. Each component interprets prompts differently, requiring specific strategies for optimal results.

Quick Research

A well-crafted objective is crucial for obtaining useful results from Amazon Quick Research. The agent breaks down objectives into sub-topics, searches across various data sources, and delivers a structured report with citations. To get actionable outputs, the objective should include the goal, audience, focus areas, timeframe, and desired output. For instance, when analyzing generative AI adoption in US hospital systems, the objective should specify the topic, scope, audience, and key outputs.

Decomposing complex topics into sub-questions helps the agent focus on specific queries and reduces the chance of pursuing irrelevant tangents. Scope the sources used by Quick Research to match the research objective, eliminating unnecessary sources to minimize noise.

Quick Flows

When crafting prompts for Amazon Quick Flows, being explicit about the what, when, and where is essential for generating precise automated workflows. Before, a vague prompt like "Create a report from our sales data" produced a generic result. After refining the prompt to include schedule, data source, calculations, output format, and delivery target, the resulting flow became more precise and actionable.

Complex logic should be structured as numbered steps to aid in debugging. Quick Flows supports conditional branching, repeat loops, and user inputs, and writing prompts as numbered sequences helps map these elements to the internal workflow structure. Iterating through conversation with Quick Flows is possible, allowing refinements to be made through follow-up instructions rather than rewriting the entire prompt.

Quick Sight

For Amazon Quick Sight analytics, structuring queries with a clear business question, relevant metrics, dimensions, and visualization preferences is essential. Including these core elements ensures that the query results in actionable insights.