Display AI model type requirements for each experiment

[raftaar1191]

What problem does this address?

Users enabling experiments don't know which AI model capabilities are required to use each feature effectively. Currently, experiment titles and descriptions don't clarify:

• Whether an experiment requires a text model (e.g., content generation, summaries)
• Whether an experiment requires an image model (e.g., alt text generation, image analysis)

This causes confusion when users enable experiments expecting them to work, only to find the AI provider they've configured doesn't support the required model type. It also prevents users from making informed decisions about which experiments to enable based on their AI provider's capabilities.

What is your proposed solution?

Display the required AI model type(s) for each experiment in the UI. Add a visual indicator (badge, label, or icon) next to each experiment title that shows which model type(s) it requires:

Example format:

☐ Excerpt Generation [Text Model]
  Generates excerpt suggestions from content

☐ Alt Text Generation [Image Model]
  Generates descriptive alt text for images using AI vision models.

☐ Image Generation [Image Model]
  Generate featured images and inline images using AI

☐ Review Notes [Text Model]
  Reviews post content block-by-block and adds Notes with suggestions for Accessibility, Readability, Grammar, and SEO.

☐ Content Summarization [Text Model]
  Summarizes long-form content into digestible overviews

☐ Title Generation [Text Model]
  Generates title suggestions from content

Implementation details:

• Add a models_required property to each experiment definition
• Display model type badges next to experiment titles (similar to WordPress plugin tags/labels)
• Use semantic colors:
  • Text Model: one color
  • Image Model: different color
• Include documentation so users understand what model types their AI provider must support

This helps users:

• Choose experiments that match their AI provider's capabilities
• Avoid enabling features that won't work with their current setup
• Understand why certain experiments may fail or produce lower quality results

[jeffpaul]

If this is the UI approach to take here, then perhaps we also look to output similar notation on the Connectors page to help make the connection for the types of models provided by enabled/displayed providers?

@dkotter curious for your thoughts here as this overlaps a bit with what we've discussed in terms of whether we show buttons/etc in the editor/media library/admin if the active provider(s) doesn't support the types of models needed.

[dkotter]

So a couple thoughts:

• One of the main concepts behind the AI Client is to abstract away the idea of specific models, as the average user isn't going to understand the differences between individual models. So I worry that this UI would just confuse most users as they may not understand the differences between each model
• Technically alt text generation is powered by a Vision or Multimodal model, not an image model, so we'd probably want to differentiate there
• And going along with that, any text generation features can also be done with a Mulitmodal model so technically only showing Text Model wouldn't be accurate

I'd almost suggest something much simpler here where we add this information into the description of each Experiment (and Feature going forward).

Example for Title Generation:

Generates title suggestions from content. Requires an AI provider that supports text generation.