Key criteria and metrics for prompt and logic adjustment
GOAL:
Explore diverse methods to present data effectively, taking into account its type and dynamic nature. Utilize a range of visualization techniques to highlight clusters within datasets, enabling to delve into specific segments for detailed analysis.
DELIVERABLES:
- Map out workflow levels, data categorization, and metric types.
- Create low-fidelity wireframes for each metric type with interaction logic at different depth levels
We started the project by defining key terminology, criteria and metrics to assess the effectiveness of AI prompt adjustment.
We organized these metrics based on their type, definition, and context of use, already gaining insights into the key decisions they inform, their potential range, and the specific patterns or outcomes we expect.
Acknowledging the model's analysis will evolve, we understood there might be patterns we haven't discovered yet.