Cross-Filter and Interaction Model Designer
Designs the interaction model for a multi-chart dashboard: cross-filtering, drill paths, and state management.
Prompt
You are an interaction designer for analytical dashboards. CONTEXT: The dashboard has charts [CHART_SET] sharing dimensions [SHARED_DIMENSIONS] for audience [AUDIENCE]. Users get confused about [INTERACTION_CONFUSION] such as which filters affect which chart. TASK STEPS: 1. Define which charts act as filter sources and which are filter targets, and which are independent. 2. Specify the cross-filter behavior: highlight, filter, or drill, for each interaction. 3. Design the drill-down hierarchy and the breadcrumb or reset mechanism. 4. Define the global filter bar versus chart-level filters and their precedence. 5. Specify the default state on load and how shareable URL state is preserved. OUTPUT FORMAT: Interaction Matrix (Source Chart -> Affected Charts -> Behavior), Drill Hierarchy, Filter Precedence Rules, Default State, and State-Sharing Spec. CONSTRAINTS: Every interaction must be discoverable and reversible; avoid filter loops where two charts filter each other ambiguously; always provide a one-click reset; the default load state must answer [PRIMARY_QUESTION] without any clicks.
How to use this prompt
- 1
Copy the prompt above and paste it into ChatGPT, Claude, or Gemini — or open it in the visual Studio to edit each part on a canvas and run it with your own key.
- 2
Replace any bracketed placeholders with your specifics. The more concrete your context and constraints, the sharper the result — see the 5-part prompt structure.
- 3
Run it, then refine. Ask the model to critique and improve its own answer with self-critique prompting.
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Open it in the visual Studio to wire it into a full workflow with your own API key — or learn the craft behind prompts like this.
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