Hey everyone,
The current AI Frontline Copilot in Tulip may utilize all available datasets for its analyses and suggestions. However, in many use cases, it is desirable or even necessary to narrow down the data basis to obtain more relevant and precise results. For example, users might want to apply the Copilot only to datasets that meet specific criteria (e.g., only active work orders, completed quality checks, machines with the status “in operation,” etc.).
Proposed Solution:
I propose adding the ability for the AI Frontline Copilot to define conditions for dataset usage. This would allow users or the developer to set filters that determine which datasets should be considered during the AI’s analysis and suggestion generation.
Benefits:
- Increased Relevance and Precision: By filtering the data basis, the AI Copilot delivers more accurate and relevant insights and suggestions tailored to the user’s specific context.
- Improved User Experience: Users can focus on the data most important for their current task, increasing efficiency.
- Flexibility and Customization: The ability to define conditions allows users to adapt the AI Copilot to various use cases and specific needs.
- Noise Reduction: Filtering out irrelevant data can help reduce “noise” in the AI Copilot’s results, highlighting clearer patterns and correlations.