Multimodal: Definition and Examples

The term multimodal refers to the ability of an artificial intelligence system to understand and produce different modalities of information simultaneously. Unlike unimodal models that process only one type of data (e.g., text only), a multimodal model can analyze an image, read a document, listen to an audio file, and respond by combining these information sources.

In the context of prompt engineering, multimodality opens up considerable possibilities. For example, you can submit a photo of a chart and ask the model to interpret it, provide a hand-drawn sketch to generate interface code, or describe a scene in text to obtain an image. Each modality brings a complementary information channel that enriches the model's understanding.

The most advanced multimodal models, such as GPT-4o, Claude (with vision), or Gemini, combine specialized encoders for each data type with a shared representation space. This allows them to reason transversely: compare text to an image, extract data from a scanned PDF, or generate a description from a video.

For the prompt engineering practitioner, mastering multimodality means knowing how to choose the right input modality for the problem, effectively combining text and visuals in the same prompt, and understanding the strengths and limitations of each channel. A well-designed multimodal prompt leverages the complementarity of modalities rather than using them redundantly.