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Multimodal

As explained in The Art of AI Product Development

A multimodal model processes or generates information across multiple modalities such as text, images, audio, or video.

A marketing application may start with text-only content generation, then face a new request: use product images alongside written content. A multimodal model can process or generate across modalities such as text, images, audio, and video. That broader capability is useful when the product experience genuinely needs more than text—not merely because the model offers it. section 5.3.5

Written content plus product images

Clients could supply copy and product images, then receive descriptions, social media posts, or suggested image edits. For an initial product focused mainly on high-quality, verifiable text, a multimodal model may be more than is needed; visual features could become a later differentiator. section 5.3.5

Before choosing a multimodal model, check:

  • Which modalities the problem and available data require. Text, visual, and auditory data call for different techniques, and model inputs must ultimately be numeric. section 3.1.1
  • Whether combining modalities improves the user experience enough to justify the added infrastructure. Handling multiple data streams increases memory, processing, and storage demands compared with a text-only model. section 5.3.5

How The Art of AI Product Development covers it

How deep it goes
Defines the capability and weighs a product example against infrastructure costs, without detailing model architecture or implementation.
Examples
Uses written marketing content and product images to illustrate generated descriptions, social media posts, and suggested image edits.
What it assumes
Assumes familiarity with language models while briefly explaining data modalities and their conversion to numeric inputs.
What you can do after
Evaluate/understand when a text-only model is sufficient and when multimodal features warrant greater infrastructure demands.
How it connects
Connects multimodal models to language models, generative AI, natural language processing, computer vision, and data modalities.

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Related concepts

This explainer was generated by Manning's knowledge graph from the text of The Art of AI Product Development. The section links point to the parts of the book it draws on.