Chart Design Principles AI Prompts

Design the annotation and labeling strategy for this chart. Chart type: {{chart_type}} Key insight to communicate: {{key_insight}} Audience: {{audience}} 1. Title and subtitle strategy: - Title: state the insight, not the data. 'Revenue grew 34% in Q3' beats 'Quarterly Revenue.' - Title should be answerable by 'so what?' — not just 'what is this?' - Subtitle: add essential context (time period, geography, unit, data source) - Format: title bold and prominent, subtitle smaller and lighter 2. Axis labels: - Label axes only when the unit is not obvious from the title or context - Remove redundant axis label if values are labeled on the chart directly - Rotate x-axis labels only as a last resort — prefer angled labels or flipping to horizontal bar chart - Units belong on the axis label or in parentheses, NOT repeated on every tick 3. Data labels (value annotations on data points): Guideline: use data labels OR axis + gridlines, not both. - Use data labels when: there are few data points and exact values matter - Use gridlines when: there are many data points and relative position matters more than exact values - For bar charts: place labels inside the bar (for long bars) or just outside (for short bars), left-aligned - For line charts: label only the final value, or highlight specific notable points - Format: use the same number formatting as the axis (1 decimal place if axis uses 1) 4. Callout annotations (highlighting specific insight): - Use sparingly: maximum 2–3 annotations per chart - Format: brief text (5–10 words) + arrow pointing to the relevant data point or region - Content: explain WHY this point is notable, not just WHAT the value is - Example: 'Spike caused by holiday campaign (Dec 15–Jan 3)' is better than 'Peak value: 42,000' 5. Reference lines and bands: - Target / goal line: show with a dashed line, labeled directly on the line - Historical average: light dashed line - Confidence interval or forecast range: semi-transparent shaded band, not heavy borders - Crisis or event periods: shaded background band with a text label at the top 6. Legend placement: - Prefer: direct labeling at the end of each line / top of each series (no separate legend) - If legend is needed: place inside the plot area (top-right or bottom-right) - Never: use a legend positioned below a wide chart that requires eye travel 7. What NOT to annotate: - Every data point (creates noise, defeats the purpose) - Obvious features the viewer can see clearly - Multiple overlapping annotations in the same chart region Return: title and subtitle for this specific chart, axis label specification, data label strategy, callout annotation text, and legend placement decision.

Help me choose the right chart type for this data and communication goal. Data description: {{data_description}} Communication goal: {{goal}} (compare, show trend, show distribution, show composition, show relationship) Audience: {{audience}} Number of data points: {{n_points}} 1. Apply the chart selection framework: COMPARISON (how do items differ?): - 2–6 categories, single metric → Bar chart (horizontal preferred for long labels) - 2–6 categories, multiple metrics → Grouped bar or radar chart - Many categories, ranked → Lollipop chart or dot plot (cleaner than dense bar charts) - Over time with few series → Line chart - Over time with many series → Heatmap or small multiples TREND (how does a metric change over time?): - Single metric → Line chart - Multiple metrics, same scale → Multi-line chart (max 4–5 lines before it becomes unreadable) - Multiple metrics, different scales → Dual-axis line chart (use with caution — can mislead) - Showing cumulative growth → Area chart - Percentage change emphasis → Slope chart DISTRIBUTION (how are values spread?): - Few data points → Dot plot or strip plot - Many data points → Histogram or density plot - Comparing distributions across groups → Box plot or violin plot - Showing outliers prominently → Box plot with jitter overlay COMPOSITION (how do parts make up a whole?): - Few parts, single time point → Pie chart (only if ≤ 5 segments, all > 5%) - Few parts, prefer comparison → Stacked bar or 100% stacked bar - Hierarchical composition → Treemap or sunburst - Changing composition over time → Stacked area chart RELATIONSHIP (how do variables correlate?): - Two continuous variables → Scatter plot - Two continuous + third variable (size) → Bubble chart - Many variable pairs → Scatter plot matrix or correlation heatmap - Categorical vs continuous → Box plot or violin plot 2. Anti-patterns to avoid: - 3D charts: distort perception — never use - Pie charts with > 5 slices: use bar chart instead - Dual-axis charts with different units: often mislead — require explicit justification - Area charts for non-cumulative data: implies accumulation — use line chart instead 3. Recommendation: - Primary recommendation with rationale - Alternative if the primary is not available in the tool - One chart type to explicitly avoid for this data and why Return: recommended chart type, alternative, anti-pattern warning, and a mockup description of what the chart should look like.

Design a color strategy for this data visualization or dashboard. Visualization type: {{viz_type}} Data encoding needs: {{encoding_needs}} (categorical groups, sequential scale, diverging scale, highlighting) Brand colors: {{brand_colors}} Accessibility requirement: {{accessibility}} (must pass WCAG AA color contrast, colorblind-safe) 1. Choose the right color palette type: CATEGORICAL (for distinguishing unordered groups): - Max 8 colors (human perceptual limit for distinct hues) - Colors should be equally distinct — avoid mixing light and dark versions of similar hues - Recommended: ColorBrewer Qualitative, Okabe-Ito (colorblind-safe), Tableau 10 - Primary choice for: bar charts with multiple categories, multi-line charts, map choropleth regions SEQUENTIAL (for ordered numeric data, low to high): - Single hue: light (low values) → dark (high values) - Or perceptually uniform multi-hue: e.g. yellow → green → blue - Avoid: rainbow (jet) colormap — perceptually non-uniform, not colorblind-safe - Primary choice for: heatmaps, choropleth maps, scatter plots with continuous color encoding DIVERGING (for data with a meaningful midpoint): - Two hues diverging from a neutral center: e.g. blue — white — red - Center (zero or baseline) must be a neutral color (white, light grey) - Primary choice for: correlation matrices, market share vs benchmark, profit/loss maps HIGHLIGHT (single color to draw attention): - Use one accent color for the key data point or series; make everything else grey - Most powerful technique for directing viewer attention 2. Colorblind accessibility: Approximately 8% of males have red-green color blindness (deuteranopia/protanopia). - NEVER rely on red vs green alone to encode meaning (e.g. positive vs negative) - Safe alternatives for red/green distinction: use blue vs orange, or add symbols/patterns - Test: convert the palette to greyscale — can values still be distinguished? - Use: Okabe-Ito palette, Viridis, Cividis — these are designed to be distinguishable by all - Tool: Sim Daltonism, Color Oracle, or Coblis for simulating colorblind views 3. Color contrast (WCAG AA): - Text on colored background: minimum contrast ratio 4.5:1 (3:1 for large text) - Data elements (bars, lines, dots): minimum 3:1 contrast ratio against the background - Test using: WebAIM Contrast Checker or Figma plugins 4. Brand color integration: - Use the primary brand color for the most important data series only - Avoid using brand colors if they are not colorblind-safe (common for red-dominant brands) - Build a secondary palette around the brand primary using ColorBrewer rules 5. Color encoding rules: - Encode one thing at a time: don't use color AND size AND shape all for different dimensions - Be consistent: the same color always means the same thing across the dashboard - Don't use color for ordinal data: use sequential shades, not arbitrary colors Return: palette specification (hex codes), palette type with rationale, colorblind test plan, contrast check, and usage rules.

Audit this chart for unnecessary visual elements and recommend how to reduce chartjunk while preserving information. Chart description: {{chart_description}} Edward Tufte's principle: maximize the data-ink ratio. Every drop of ink should be earning its place by communicating data. Remove everything else. 1. Elements to audit and recommendations: GRIDLINES: - Remove: dense gridlines that compete with the data - Keep: light, sparse reference gridlines (every major interval, not every minor one) - Better: label the key data points directly rather than requiring gridline reference AXIS LINES: - Remove: the heavy axis frame / box around the chart (chartjunk) - Keep: the y-axis line if bars/lines need a baseline reference - Remove: both axes in scatter plots (replace with reference lines at means if needed) TICK MARKS: - Remove: tick marks that just repeat the gridline - Keep: tick marks only where they aid reading (longer ticks at major intervals) BACKGROUNDS: - Remove: shaded chart backgrounds (grey, blue — adds no information) - Remove: gradient fills on any element - Keep: white or transparent background LEGENDS: - Prefer: direct labeling at the end of lines / top of bars over a separate legend - Remove: legends when there is only one data series - If legend is needed: place inside the chart area, not in a separate box BORDERS AND SHADOWS: - Remove: borders around charts, shadows on bars, rounded corners on bar charts - Remove: drop shadows on any element DECORATIVE ELEMENTS: - Remove: clip art, icons, 3D effects, excessive color - Remove: chart titles that are just labels (e.g. 'Bar Chart of Revenue') — replace with insight title COLOR: - Remove: color used for decoration rather than encoding data - Use: a single color for single-series charts - Use: color to highlight only the key point 2. Before vs after assessment: - List each element present in the current chart - Mark each: Keep / Remove / Simplify - Estimate the data-ink ratio improvement (rough %) 3. The one change with the biggest impact: - What single change would most improve this chart's readability? Return: element-by-element audit table, removal recommendations, and a description of the simplified version.

Design a small multiples layout for this dataset instead of a cluttered single chart. Dataset: {{dataset_description}} Dimension to facet by: {{facet_dimension}} (region, product, segment, etc.) Number of facets: {{n_facets}} Key metric: {{metric}} Small multiples (trellis charts, faceted charts) show the same chart type repeated for each value of a dimension. They enable comparison across groups while keeping each individual panel clean and readable. 1. When to use small multiples vs overlaid series: Use small multiples when: - More than 4–5 series on a single chart becomes tangled (spaghetti chart) - The comparison is across rather than within the dimension - Patterns within each panel are more important than the exact values between panels Use overlaid series (single chart) when: - You need exact value comparison between series at the same time point - There are only 2–3 series and they don't overlap heavily 2. Layout design: - Grid arrangement: prefer rows × columns that are wider than tall (landscape orientation) - Panel count guideline: 4–12 panels is the readable range. > 20 panels requires a different approach. - Panel size: large enough to show the pattern clearly, small enough that all panels fit on one screen/page - Aspect ratio: each panel should follow the banking to 45° rule — line slopes close to 45° are most readable 3. Shared axes (critical for comparability): - ALL panels must share the same x and y axis scales unless explicitly communicating within-panel patterns - Do NOT use independent (free) scales unless the goal is to show pattern shape, not magnitude - If scales differ substantially between panels: use a log scale or explicitly label each panel's scale range 4. Ordering of panels: - By magnitude: sort panels by the most meaningful summary statistic (e.g. total revenue) - Alphabetically: only if magnitude order is not meaningful - By natural order: time, geography, hierarchy - Never: random order 5. Labeling in small multiples: - Panel titles: short and above each panel (not below) - Remove x-axis labels from all but the bottom row - Remove y-axis labels from all but the leftmost column - Shared axis titles: one title spanning the entire grid edge, not repeated per panel - Highlight a reference pattern: add a light grey copy of the overall average/total in each panel for reference 6. Highlighting across panels: - Use the same highlight color in each panel to emphasize the same element (e.g. the current period) - Add a reference line at the overall average in each panel so viewers can see which panels are above or below Return: layout specification (rows × columns, panel size), axis sharing rules, panel ordering recommendation, and labeling specification.