Scientific AI Prototyping Framework
Bridging the technical gap between AI engineering and product design with a published, world-class framework.
Impact
Q1
Journal
Fortune 500
Usage
Impact
Scientific paper published in top-tier journal, validating the framework’s novelty and utility. Framework utilized in consulting for industry giants like Adobe and Siemens.
Process
The core problem was “LLM erraticism”: Designers couldn’t predict how AI would behave. We developed a methodology to map probabilistic outputs to deterministic UI states.
- Mapping Uncertainty: Quantifying AI confidence levels in design files.
- Dynamic State Logic: Designing UIs that adapt to low-confidence AI predictions.
- Implementation Guidelines: Creating a shared language for devs and designers.
Methods
- Human-Computer Interaction
- Prompt Engineering
- UML Design
- Prototyping Strategy