AI BriefingAthena: An Intermediate Representation for Iterative Scaffolded App Generation with LLMs
Key point
It structures LLM app generation using storyboards, data models, and GUI skeletons.
Details
Generating a complete user interface with an LLM in a single pass is not easy. Screen composition, navigation between screens, and the data model are all intertwined, making it hard to fit sufficient context into a single prompt, and the resulting output often collapses into one giant file.
Athena is a prototype application generation environment that introduces shared intermediate representations to solve this problem. Developers work together with an app storyboard, a data model, and GUI skeletons, iteratively interacting with the LLM to progressively complete the app.
These intermediate representations structure the generation process, helping produce code organized into multiple files while also reducing errors. The paper explains that this enables more organized code generation.
The key points are as follows.
- Organize screen flow first with an app storyboard.
- Align the app's overall data structure with a data model.
- Generate the skeleton of each screen separately with GUI skeletons.
- As a result, scaffold the LLM to better produce multi-file structured code.
In a user study, 75% of participants preferred the Athena prototype over a typical chatbot-style baseline.
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