Vision Prompting
Vision prompting writes instructions for multimodal models that can understand images. Instead of describing what you want generated, you describe what you want observed: objects, layout, text, style, and relationships within an uploaded image.
How It Works
Upload or reference an image in the prompt.
Ask specific questions about its content.
Request structured output: tables, lists, coordinates, or JSON.
Combine image analysis with reasoning or generation tasks.
Real Example
"Analyze this product screenshot. List the visible UI elements in a table with columns: Element, Position, Purpose. Then suggest three usability improvements referencing specific elements."
Best Practices
- Ask one focused question per image region or aspect.
- Request structured output for complex analyses.
- Provide context about the image origin or purpose.
- For OCR tasks, ask for verbatim text extraction first.
Common Mistakes
- Vague questions like "describe this image".
- Assuming the model sees what you think is obvious.
- Not specifying output structure for analysis tasks.
When to Use Vision
Prompts using Vision Prompting
Ready-to-copy examples of this technique in action.
Frequently Asked Questions
When should I use Vision Prompting?
UI/UX review, Document and receipt extraction, Educational analysis. If your task is more complex or your output is inconsistent, consider combining it with examples or chain-of-thought.
Does Vision Prompting work on all AI models?
Most prompting techniques transfer across modern models like ChatGPT, Claude, and Gemini, though results vary. Test the same prompt on two models and keep the best performer.
What is the next technique to learn after Vision?
Based on your related techniques, explore: Prompt Basics, Image Prompting, Multimodal Prompting.
How do I practice Vision?
Use the prompts above as starting points, then modify one variable at a time. Track which changes improve your results in a prompt library.