5.0 KiB
Infographic Workflow
Create data visualizations, explainers, and statistical infographics using the 6-step editorial process.
When to Use
- Explaining concepts or processes
- Visualizing data or statistics
- Creating how-to guides
- Summarizing reports or research
- Making comparisons
6-Step Process
Step 1: Extract Narrative
Goal: Understand the complete story being told.
Questions to answer:
- What is the main concept or data being explained?
- What is the key insight or takeaway?
- Who is the target audience?
- What action should viewers take?
Output: 2-3 sentence summary of the narrative.
Step 2: Derive Visual Concept
Goal: Translate narrative into a single visual metaphor.
Guidelines:
- Choose 2-3 physical objects that represent the concept
- Prefer familiar, universal metaphors
- Avoid abstract shapes without meaning
- Consider spatial relationships (hierarchy, flow, comparison)
Examples:
- Data growth → Plant/tree growing
- Security → Shield/lock
- Process → Pipeline/conveyor belt
- Comparison → Balance scale
Output: Visual metaphor description.
Step 3: Apply Aesthetic
Goal: Define the visual style.
Recommended for infographics:
- Colors: Muted palette with 1-2 accent colors
- Style: Flat design, clean lines
- Typography: Sans-serif, clear hierarchy
- Layout: Clear sections, visual flow
- Icons: Simple, consistent style
Output: Style description (2-3 sentences).
Step 4: Construct Prompt
Goal: Build the generation prompt.
Template:
Create an infographic explaining [topic].
Visual concept: [metaphor from Step 2]
Key elements:
- [Main data point or concept]
- [Supporting element 1]
- [Supporting element 2]
Style: [aesthetic from Step 3]
Layout: [horizontal/vertical], [sections description]
Text to include:
- Title: "[title]"
- Key stat: "[number or fact]"
- [Other text elements]
Output: Complete prompt.
Step 5: Generate
Command:
uv run scripts/generate.py "[prompt]" output.png 16:9 2K
Settings for infographics:
- Aspect ratio: 16:9 (landscape) - best for infographics
- Size: 2K minimum - ensures text readability
- Model: gemini-3-pro-image-preview (Nano Banana Pro)
Step 6: Validate
Validation criteria:
| Criterion | Check |
|---|---|
| Text legibility | All text is readable at 100% zoom |
| Data accuracy | Numbers/facts are displayed correctly |
| Visual hierarchy | Eye naturally flows through content |
| Color contrast | Sufficient contrast for accessibility |
| Completeness | All key elements are present |
| Brand alignment | Matches intended style |
If validation fails:
- Identify specific issues
- Modify prompt to address them
- Regenerate
- Maximum 3 iterations
Example Workflow
Request: Create an infographic about how neural networks learn.
Step 1: Extract Narrative
"Neural networks learn by adjusting connection weights through forward propagation and backpropagation. Key insight: the process is iterative and improves over time. Audience: Technical beginners."
Step 2: Visual Concept
"A network of interconnected nodes with signals flowing through, showing adjustment dials on connections. Like a city's road network with traffic lights being adjusted."
Step 3: Aesthetic
"Flat design with dark blue background, bright connection lines in cyan and orange. Minimal, clean style with clear node shapes."
Step 4: Prompt
Create an infographic explaining how neural networks learn.
Visual concept: Network of connected nodes with adjustment dials on connections, signals flowing through like traffic.
Key elements:
- Input layer with data entering
- Hidden layers with connection weights
- Output layer with result
- Feedback loop showing backpropagation
Style: Dark blue background, cyan and orange accents, flat design, clean minimalist style.
Layout: Horizontal flow from left (input) to right (output), with backpropagation arrow below.
Text to include:
- Title: "How Neural Networks Learn"
- Labels: "Input", "Hidden Layers", "Output", "Backpropagation"
Step 5: Generate
uv run scripts/generate.py "Create an infographic explaining how neural networks learn..." neural_network.png 16:9 2K
Step 6: Validate
- Text readable
- Flow is clear left-to-right
- Colors have good contrast
- All labels present
Tips for Better Results
- Simple prompts often work best - "Infographic explaining X" can produce excellent results
- Model understands context - It will add relevant icons/imagery automatically
- Be specific about text - Include exact wording for titles and labels
- Iterate with conversation - Ask for specific changes after initial generation
- Use reference images - For style consistency across multiple infographics
Common Issues
| Issue | Solution |
|---|---|
| Text too small | Increase size to 4K or reduce text amount |
| Cluttered layout | Simplify to fewer elements |
| Wrong style | Be more explicit about aesthetic |
| Missing elements | List all required elements explicitly |