What is Multi-Modal Prompting?
Multi-modal prompting is a technique that enables AI models to process and respond to multiple types of input media, such as combining text with images, audio, or documents. Unlike traditional text-only prompts, multi-modal prompting allows for richer interactions by incorporating visual context, enabling applications like image analysis, document processing, and visual reasoning.Why Use Multi-Modal Prompting?
- Enhanced Context: Visual content provides information that’s difficult to express in text alone
- Improved Accuracy: Models can “see” what they’re analyzing rather than relying on descriptions
- Complex Reasoning: Combines visual perception with textual reasoning for sophisticated tasks
- Natural Interaction: Mimics human ability to process multiple sensory inputs simultaneously
- Application Versatility: Enables new use cases like visual QA, document analysis, and content moderation
- Reduced Ambiguity: Images provide concrete references that minimize misinterpretations
Basic Implementation in Latitude
Here’s a simple multi-modal prompting example using Latitude:Image Analysis
input_image
parameter will automatically be configured with an image upload button, while the analysis_request
will be a standard text field. This configuration is based on the parameter types specified in the YAML header.
Advanced Implementation with Multiple Media Types
This example shows how to work with both images and text in a more sophisticated workflow:Setting Up Multi-Modal Parameters
Latitude offers several ways to configure and use multi-modal parameters:Parameter Configuration in YAML
You can define parameter types directly in your prompt’s configuration YAML:Parameter Input Methods in the Playground
When testing multi-modal prompts in the Playground, you can use these input methods:- Manual Upload: Directly upload images and files through the parameter input fields
- Dataset Integration: Load images from a dataset for batch testing across multiple visual examples
- History Reuse: Access previously used images from your parameter history
Working with Multi-Modal Inputs in the Playground
When testing multi-modal prompts in the Latitude Playground, you’ll encounter specific controls for image and file inputs:-
Image Parameters:
- Click the upload button to select an image from your device
- Optionally, use the image preview to verify you’ve selected the correct file
- For vision models, images will be appropriately encoded and embedded in the prompt
-
File Parameters:
- Upload PDF documents or other supported file types
- The Playground will process these files according to the provider’s requirements
- Some providers may have file size or type limitations
-
Dataset Testing:
- Create datasets that include image or file URLs for batch testing
- Test your multi-modal prompts across a variety of visual inputs
- Compare performance with different visual content types
Document Processing Implementation
This example demonstrates how to process document files (PDFs) using multi-modal capabilities:Best Practices for Multi-Modal Prompting
Image Input Optimization
Image Input Optimization
Image Quality Guidelines:
- Resolution: Provide images with sufficient resolution (min. 512px on shortest side)
- Clarity: Ensure images are well-lit and in focus
- Framing: Center the relevant subject in the frame
- Compression: Minimize compression artifacts that might affect analysis
- Aspect Ratio: Use standard aspect ratios when possible
- Photographs: Work best for general object/scene identification
- Screenshots: Useful for UI analysis, but text may be analyzed separately
- Diagrams/Charts: Effective for technical analysis
- Illustrations: May be interpreted differently than photographs
Prompt Structure
Prompt Structure
Text-Image Integration:
- Place image reference before the main question/instruction
- Clearly separate image and text with formatting
- Reference the image explicitly in your instructions
- Be precise about what aspect of the image to focus on
- Ask specific questions rather than open-ended ones
- Break complex visual tasks into simpler components
- Use bullet points for multiple questions about the same image
- Provide context before questions to prime the model’s attention
Task-Specific Guidance
Task-Specific Guidance
Visual Classification:
- Specify the taxonomy or categories you want used
- For ambiguous cases, ask for confidence levels
- Request alternative classifications when appropriate
- Frame questions to focus attention on relevant image areas
- Provide examples of desired answer format/detail level
- For subjective questions, specify the perspective to take
- Indicate desired detail level (brief vs. comprehensive)
- Specify aspects to focus on (colors, shapes, text, etc.)
- Request structured output for consistent descriptions
Technical Considerations
Technical Considerations
Model Selection:
- Choose models specifically designed for multi-modal tasks
- Consider context window limitations when sending large images
- Test smaller/lighter models for simpler visual tasks
- Resize very large images before sending
- Consider black and white conversion for non-color tasks
- Use image cropping to focus on relevant regions
- Test with multiple prompt variations for optimal results
Advanced Techniques
Visual Chain-of-Thought
Implement a step-by-step visual reasoning process:Visual CoT
Multi-Image Comparison
Compare and analyze multiple images simultaneously:Visual Information Extraction
Extract structured data from visually rich content:Visual Extraction
Related Techniques
Multi-modal prompting works well when combined with other prompting techniques:- Chain-of-Thought: Break down visual reasoning into explicit steps for complex image analysis.
- Self-Consistency: Generate multiple interpretations of an image and select the most consistent one.
- Template-Based Prompting: Use templates to standardize visual analysis across different images.
- Retrieval-Augmented Generation: Combine image analysis with retrieved textual information for contextual understanding.
- Few-Shot Learning: Provide examples of image-text pairs to guide the model’s visual interpretation.
Real-World Applications
Multi-modal prompting is particularly valuable in these domains:- Content Moderation: Analyzing images for policy violations or inappropriate content
- E-commerce: Automated product photo analysis, comparison, and description generation
- Healthcare: Reviewing medical images alongside patient records (with appropriate regulatory compliance)
- Document Processing: Extracting information from forms, receipts, and ID documents
- Accessibility: Generating detailed image descriptions for vision-impaired users
- Education: Creating interactive learning experiences with visual elements
- Quality Control: Inspecting products or materials for defects or compliance issues
Advanced Configuration for Multi-Modal Parameters
Parameter Definition Options
Parameters in multi-modal prompts can be configured with several advanced options:Parameter Types for Multi-Modal Inputs
Latitude supports these parameter types for multi-modal prompting:-
Image Parameters (
type: image
):- Supported by models with vision capabilities (e.g., GPT-4o, Claude 3)
- Rendered in the prompt using
{{ parameter_name }}
- Appears as an image upload button in the Playground
- Most models support common formats: JPEG, PNG, WebP, GIF
-
File Parameters (
type: file
):- Supported by models with document processing capabilities
- Different providers support different file types:
- Claude: PDF documents
- GPT-4o: Various document formats
- Enables document analysis, data extraction, and PDF processing
-
Text Parameters (
type: text
):- Standard text input that can be used alongside multi-modal inputs
- Can contain instructions for processing the visual content