Multimodal Prompting: Working with Images and Text Together

The era of text-only AI is over. Modern language models like GPT-4V, Claude 3.5, and Gemini can understand images, documents, charts, and screenshots alongside text. Multimodal prompting is the art of crafting prompts that effectively combine visual and textual information to accomplish tasks that neither modality could handle alone. This guide covers the principles, techniques, and best practices for getting the most out of vision-language models.

What Is Multimodal Prompting?

Multimodal prompting involves sending both images and text to an AI model, along with instructions for how the model should process and relate the visual and textual information. The text component guides the model's attention, specifies what to look for in the image, and defines the desired output format.

This is fundamentally different from image generation prompts, which describe an image you want created. Multimodal prompting is about understanding and analyzing existing images, though the line between analysis and generation is increasingly blurry as models become more capable.

"Multimodal prompting unlocks an entirely new category of AI applications: from automated quality inspection and document processing to visual accessibility and design critique."

Effective Multimodal Prompt Patterns

Directed Analysis

Instead of asking the model to "describe this image," direct its attention to specific aspects you care about. The more specific your direction, the more useful the analysis:

Analyze the attached dashboard screenshot and provide:
1. The three KPIs that show the most significant change
2. Any data visualization best practice violations
3. Accessibility issues (contrast, color-only encoding, etc.)
4. Specific recommendations for improving readability

Focus on actionable insights, not general description.

Comparison Prompts

Send multiple images and ask the model to compare, contrast, or track changes between them. This is powerful for design reviews, A/B testing analysis, and progress tracking. Be specific about the dimensions of comparison you care about.

Extraction and Digitization

Use multimodal prompts to extract structured data from images of documents, receipts, whiteboards, handwritten notes, or screenshots. Specify the exact fields to extract and the output format:

Extract all information from this receipt image and return as JSON:
{
  "store_name": "",
  "date": "YYYY-MM-DD",
  "items": [{"name": "", "quantity": 0, "price": 0.00}],
  "subtotal": 0.00,
  "tax": 0.00,
  "total": 0.00,
  "payment_method": ""
}

Use Cases Across Industries

• Healthcare: Analyzing medical images, extracting data from clinical documents, and generating reports from visual findings.
• E-commerce: Automated product categorization, quality inspection, and generating descriptions from product photos.
• Education: Solving problems from textbook photos, analyzing student work, and creating accessible descriptions of visual content.
• Real estate: Analyzing property photos, extracting floor plan details, and generating listing descriptions.
• Software development: Converting UI designs to code, analyzing error screenshots, and reviewing visual test results.

Limitations and Considerations

Current multimodal models have important limitations you should factor into your workflows:

• Spatial reasoning: Models can struggle with precise spatial relationships, counting objects, and reading small text in images.
• Hallucination: Models may describe elements that are not present in the image, especially when asked about specific details.
• Resolution sensitivity: Very high-resolution images may be downsampled, losing detail. Very small details may be missed entirely.
• Consistency: The same image analyzed multiple times may produce slightly different descriptions each time.

Best Practices for Multimodal Prompting

1. Be specific about what to analyze: Direct the model's attention to the specific parts of the image that matter for your task.
2. Ask for structured output: Request specific formats like JSON, tables, or categorized lists rather than free-form descriptions.
3. Provide context: Tell the model what type of image it is looking at and what domain-specific knowledge to apply.
4. Verify critical details: Always double-check AI-extracted data from images, especially numbers, dates, and names.
5. Optimize image quality: Ensure images are clear, well-lit, and at an appropriate resolution for the task.