Full Dashboard Design Chain AI Prompt

Step 1: Requirements — define the dashboard's purpose in one sentence. Identify the audience (technical level, role, decision they need to make). List the top 5 questions the dashboard must answer. Define the data sources and refresh frequency.
Step 2: KPI selection — from all available metrics, select the 3–5 most important for the stated purpose. For each: define the metric precisely, specify the comparison periods, identify the direction of good (up or down), and assign an owner.
Step 3: Layout design — sketch the dashboard layout (rows and columns). Apply F-pattern reading order. Assign each chart to a position based on importance. Specify filter placement, whitespace, and scroll behavior.
Step 4: Chart design — for each chart position, specify: chart type (using the chart selection framework), data it displays, color encoding, axis labels, data labels vs gridlines decision, and title (insight statement, not label).
Step 5: Color and typography — define the color palette (categorical, sequential, diverging). Check all colors for colorblind safety and WCAG contrast compliance. Specify font, font sizes, and number formatting.
Step 6: Interactivity design — specify: which elements are clickable (and what happens), filter behavior (what each filter affects), drill-down paths, and tooltip content for each chart.
Step 7: Performance and accessibility — estimate query count and data volume. Identify pre-aggregation opportunities. Write alt text for each chart. Verify keyboard navigability. Document the refresh schedule and caching strategy.
Step 8: Review and iteration — list 3 questions to ask stakeholders in the first review. Define the success criteria: what would make this dashboard 'done'? Specify how usage will be measured post-launch.