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Prompt Engineering
- Introduction
- Model Basics
- Prompt Structures
- Clarity & Specificity
- Using Context
- Role Instructions
- Step-by-Step
- Handling Ambiguity
- Creativity vs Precision
- Using Examples
- Advanced Techniques
- Troubleshooting
- Common Pitfalls
- Evaluating Quality
- Real-World Examples
- Prompt Templates
- AI Tasks
- Safety & Ethics
- Multimodal Prompts
- Data Extraction
- Conversation
- Personalization
Multimodal Prompts
Some AI models can process both text and images. Prompts can include instructions for both, allowing you to create richer and more interactive experiences. Multimodal prompts are useful for tasks like image captioning, visual question answering, and combining data from different sources.
Why Use Multimodal Prompts?
- Enhanced capabilities: Combine text, images, and other data types for more comprehensive outputs.
- Broader applications: Useful for education, accessibility, creative projects, and more.
- Improved user experience: Allows users to interact with AI in new and engaging ways.
Example
Describe the image and summarize the following text:
[Insert image here]
[Insert text here]Expanded Example:
You are an art critic. Analyze the attached painting, describing its style, colors, and emotional impact. Then, summarize the artist's statement provided below in 2-3 sentences.
[Insert image here]
[Insert artist statement here]Check your model's documentation for supported input types. Not all models can process images, audio, or other modalities.
Best Practices for Multimodal Prompts
- Clearly separate instructions for each input type (e.g., "For the image... For the text...").
- Provide context for how the different inputs relate to each other.
- Test your prompt with different combinations of inputs to ensure reliability.