The system will help users to generate text for one or multiple placeholders depending on the element context.
Technology readiness | Risks | Complexity |
---|---|---|
🟢 Ready for implementation | 🟡 Moderate risk |
🟢 Light complexity |
Open-source LLMs are already capable of working with this task, like open-llms [Github]. A solution like privateGPT [Github] gives the ability to work offline with local files, increasing the security of the solution.
Step #1) User selects elements or pages to populate with text
Step #2) System extracts information about the number and context of the module
Step #3) Based on input parameters, the Language model generates the output
Step #4) User verifies the output and approves the insertion
Figma plugins presented here mostly demonstrate the possible interface of the implemented solution.
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FigGPT [Figma Plug-in] [Website]
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Cube GPT - UX AI Assistant [Figma Plug-in]
🟢 Pros
- This approach would significantly streamline the process of generating text for placeholders, saving time and effort for designers.
- The system can adapt to different element contexts, ensuring the generated text is relevant and fits the design's purpose.
- With technologies like privateGPT, the system can work offline with local files, providing an added layer of data security.
🔴 Cons
- The success of this feature heavily relies on the system's ability to accurately determine the context of the module. That context should be pre-processed for the system, like count and type of placeholders, name, the functionality of the element, etc.
- AI can generate text based on context, but understanding the designer's precise intent or the emotion they wish to convey might be challenging. Especially in the visual domain.