Data Storytelling AI Prompts

Design a visualization that effectively communicates before-and-after comparison. Scenario: {{scenario}} (policy change, product launch, intervention, etc.) Before period: {{before}} After period: {{after}} Metric: {{metric}} 1. Chart options for before/after: Slope chart (most effective for 2-point comparison): - Two vertical axes (Before | After) - Each entity is a line connecting its before value to its after value - Lines going up = improved, lines going down = declined - Color-code by direction: green for improvement, red for decline, grey for neutral - Best for: comparing many entities before/after a single event Paired bar chart: - Two bars per entity (before in grey, after in color) - Sort by the change magnitude (largest improvement first) - Add a difference annotation between each pair - Best for: few entities with absolute value comparison needed Dumbbell chart: - A horizontal dot plot variant - One dot for before, one dot for after, connected by a line - Dot size can encode a third variable (e.g. sample size) - Best for: clean, minimal presentation of many entities Difference area chart: - Two lines (before and after) with the area between them shaded - Green shading where after > before, red shading where after < before - Best for: showing the cumulative effect over time Bump chart: - Showing rank changes rather than absolute changes - Lines connecting before-rank to after-rank for each entity - Best for: rankings, leaderboards, or relative position changes 2. Choosing the right chart: - 2 entities: paired bar or slope chart - 3–10 entities: slope chart or dumbbell - > 10 entities: dumbbell or filtered slope (highlight top/bottom movers) - Rank focus: bump chart - Time series with intervention line: annotated line chart with shaded 'after' region 3. Statistical context: - Is the before-after difference statistically significant? - Add error bars or confidence intervals where sample sizes are small - Label the change magnitude on the chart (not just the before and after values) 4. Intervention line: - On a time series: mark the exact intervention date with a vertical dashed line - Label the line: 'Campaign launch (Mar 15)' - Shade the before period lightly grey, the after period white Return: recommended chart type with rationale, design specification, and statistical context requirements.

Design a set of 3–5 charts for an executive presentation on this topic. Topic: {{topic}} Key message: {{key_message}} Audience: C-suite / senior leadership Time available: {{time}} minutes Data available: {{data_available}} Executive audiences have specific needs: they want the conclusion first, they need context without detail overload, and they make decisions — so every chart must point to an action. 1. Chart 1 — The headline chart (30 seconds): - Must communicate the single most important finding - Should be readable in < 5 seconds - Title IS the conclusion: 'EMEA Revenue Is 23% Below Target — Driven by Germany' - Minimal detail — highlight only the critical element - Maximum 1 annotation 2. Chart 2 — The context chart (60 seconds): - Shows the trend or baseline that explains why the headline matters - Answers: 'Is this getting better or worse?' - Time series with the key threshold or target line shown 3. Chart 3 — The breakdown chart (60 seconds): - Decomposes the headline into its components - Answers: 'Where is this concentrated?' - A ranked breakdown by the most actionable dimension (by team, by product, by region) 4. Chart 4 (optional) — The root cause chart (60 seconds): - Shows what is driving the breakdown - Answers: 'Why is this happening?' - A correlation, funnel, or attribution chart 5. Chart 5 (optional) — The implication chart (30 seconds): - Projects the impact if no action is taken - Or shows the potential gain if the recommended action is taken - Answers: 'What happens if we do / don't act?' 6. Executive chart design rules: - Use only one key message per chart — no chart with two conclusions - No table with more than 5 rows (executives do not read tables in presentations) - Font size minimum 18pt for any text in the chart - Never show error bars, confidence intervals, or p-values in executive presentations - Remove every axis, gridline, and label that is not strictly necessary - Color: use brand colors + one highlight color only Return: chart set specification (title, chart type, data elements, key message per chart) and presentation flow narrative.

Build a visual narrative around this data insight for a presentation or report. Key insight: {{insight}} Supporting data: {{data}} Audience: {{audience}} Medium: {{medium}} (slide deck, scrollytelling web page, printed report, video) 1. The one-chart story structure: Every visualization that tells a story has three parts: - Setup: what is the context? What should the audience expect or know before seeing the data? - Tension: what is the surprising or important finding? - Resolution: what does this mean and what should we do? Apply these three parts to my specific insight. 2. Chart sequence for presentations (one chart per slide): Slide 1 — Context chart: - Show the baseline situation: the overall trend or the 'before' state - No highlighting yet — just the context - Title: a neutral statement of what is shown Slide 2 — Revelation chart: - Same chart, but now highlight the key finding - Grey out everything except the critical data point, region, or series - Title: the insight stated as a declarative sentence - Annotation: 1–2 callouts explaining the highlighted element Slide 3 — Implication chart: - A different chart showing the consequences or the 'so what' - If the finding is a problem: show the cost or impact - If the finding is an opportunity: show the potential gain - Title: the action or question this finding raises 3. Progressive disclosure technique: - Build the chart incrementally: start with one line/bar, add the others one by one - Each addition is a new point in the story - Reveal the key comparison last, after the audience understands the baseline 4. The scrollytelling version (for web): - Section 1: static chart with neutral context - Section 2: as user scrolls, highlight the anomaly - Section 3: annotate with the explanation - Section 4: transition to a different view showing the implication 5. What NOT to do: - Do not show all the data at once and hope the audience finds the insight - Do not use a single chart with 5 callouts — one chart, one point - Do not let the title be a label ('Revenue by Quarter') — make it the insight Return: three-slide story structure with specific chart descriptions, title for each slide, and annotation text.

Design visualizations that communicate uncertainty, ranges, and confidence intervals without misleading the audience. Data type: {{data_type}} (forecast, survey results, experimental results, model output) Audience: {{audience}} Uncertainty type: {{uncertainty_type}} (sampling uncertainty, forecast range, measurement error) 1. Why uncertainty visualization matters: - A single line or point estimate implies false precision - Decision-makers need to understand the range of plausible outcomes - But: uncertainty visualizations are often misread — design for correct interpretation 2. Chart options for uncertainty: Error bars: - Show ±1 SD, ±1 SE, or 95% CI around a point estimate - Common mistake: error bars are often mislabeled — always state what they represent in the caption - Better for: point estimates with few comparisons Confidence bands: - Semi-transparent shaded region around a line - The line represents the central estimate; the band is the interval - Use low opacity (20–30%) to avoid obscuring the data - Better for: time series forecasts with uncertainty growing over time Gradient bands: - Multiple nested bands for different confidence levels (e.g. 50%, 80%, 95%) - Darker center = higher confidence; lighter outer = wider range - Better for: forecast fans where showing multiple scenarios is important Violin plots: - Show the full probability distribution of values for each group - Better than box plots for showing bimodal or skewed distributions - Combine with a box plot overlay to show median and quartiles Dot plots with distribution: - Individual observations as dots with a density curve overlay - Shows both the spread and any outliers - Better than summary statistics alone for small samples Hypothetical outcome plots (HOPs): - Animate multiple possible outcomes sampled from the distribution - Studies show HOPs lead to more accurate uncertainty interpretation than static bands - Suitable for: interactive web visualizations, not static reports 3. Common mistakes to avoid: - Showing only the point estimate without any range - Using error bars without labeling what they represent - Showing 95% CI and calling it 'possible range' — it excludes 5% of outcomes - Making the uncertainty band so wide it is useless - Hiding uncertainty because it 'looks bad' — this is dishonest and dangerous 4. Language for uncertainty: - Label: '95% confidence interval' not 'margin of error' (unless you mean exactly that) - Caption: always explain what the shaded region represents in plain language - For forecasts: show a plain-language probability statement: 'There is a 20% chance of exceeding $10M' Return: recommended uncertainty visualization for this specific data, design specification, labeling language, and a caption explaining the uncertainty to a non-technical audience.