Choose the right chart type for your data and get specific design recommendations for clear, impactful visualizations.
Copy & paste the prompt below into your preferred LLM. Unless a specific AI model is mentioned, you can use whichever you prefer.
You are a data visualization expert. Help me choose and design the right chart for my data. My data: - What am I showing: [DESCRIBE YOUR DATA AND THE STORY YOU WANT TO TELL] - Data type: [TIME SERIES / COMPARISON / DISTRIBUTION / COMPOSITION / RELATIONSHIP / GEOGRAPHIC / FLOW] - Number of variables: [HOW MANY DIMENSIONS?] - Data points: [APPROXIMATE NUMBER OF ROWS/RECORDS] - Audience: [EXECUTIVE / TECHNICAL / GENERAL PUBLIC / ACADEMIC] - Tool: [EXCEL / GOOGLE SHEETS / TABLEAU / POWER BI / PYTHON / R / D3.JS / OTHER] - Context: [PRESENTATION / REPORT / DASHBOARD / PUBLICATION / SOCIAL MEDIA] Deliver: 1. **Chart Recommendation:** The best chart type with reasoning. Include 2 alternatives with pros and cons 2. **Design Specifications:** - Color palette (accessible, colorblind-friendly) - Axis labels and titles - Legend placement - Data label strategy (when to show values, when to omit) - Gridlines and reference lines - Annotations for key insights 3. **What to Avoid:** Chart junk, misleading scales, unnecessary 3D, pie charts for too many categories — specific to your data 4. **Implementation:** Step-by-step instructions to create this chart in your tool 5. **Dashboard Context:** If this is part of a dashboard, how it relates to companion charts 6. **The Insight Sentence:** The one sentence this chart should communicate. If someone glances at it for 5 seconds, this is what they should understand 7. **Accessibility:** Alt text for the chart, high-contrast version, data table fallback A chart should make the data's story obvious, not require explanation.
"Data Visualization Recommender" applies research-backed prompting principles: depth requirements and reasoning elicitation. These are the same techniques used by professional prompt engineers to get predictable, high-quality results. Expect actionable analytical insights with methodology documentation and visualization recommendations. The constraints in this prompt prevent the model from falling back on vague, unhelpful responses.
These data analysis tips will help you get stronger results when using "Data Visualization Recommender" and similar prompts in this category.
"Data Visualization Recommender" is particularly useful in these situations. If any of these scenarios sound familiar, this prompt will save you significant time.
When you use "Data Visualization Recommender" with ChatGPT, Claude, or Gemini, here is what to expect in the AI output.
Adapt "Data Visualization Recommender" to your specific situation by modifying these key areas. The more context you add, the better the results.