Prompt engineering by Quick component: Patterns and pitfalls

This article provides component-specific prompt engineering patterns and best practices for maximizing Amazon Quick capabilities across Research, Flows, Sight, chat agents, and action integrations.

• Quick Research: Define goals with audience context, decompose complex topics into sub-questions, and scope sources to reduce noise.
• Quick Flows: Be specific about timing and conditions, structure complex logic as numbered steps, and iterate through conversation rather than rewrites.
• Quick Sight: Include business questions, metrics, dimensions, time periods, and visualization types in analytics queries for precise results.
• Chat agents: Define clear identity with expertise boundaries, connect relevant knowledge sources, and design suggested prompts that model specificity.
• Action integrations: Provide complete parameters upfront, structure multi-step workflows with clear dependencies, and include review steps for destructive operations.
• Common pitfalls: Vague language, overloaded prompts, missing context, unspecified output formats, and inadequate testing across all components.

Effective prompt engineering requires precise communication of intent tailored to each component's strengths, with documentation and iteration for continuous improvement.