Verify AI-Generated Data Analysis

User problem

Users frequently need to debug issues with their charts, understand how their data is being processed, and confirm the accuracy of the results. Current tools for this are limited to traditional code debugging methods, like adding print statements, which are often not intuitive or accessible for users with data literacy but not deep coding expertise. This lack of transparency can lead to anxiety that the data analysis is a "black box" done entirely by AI, rather than by the code the AI generates. Users need a clear, approachable, and detailed way to see the step-by-step logic of the data transformation.

What is it?

The Logs tab is a new, fourth interface pane in the chart editor, appearing alongside Preview, Spec, and Code. It serves as a visual, step-by-step record of data processing and application events that occur when a chart or component runs. The design prioritizes being approachable and scannable over looking like traditional "Matrix" code logs, making it suitable for users who are not professional developers. It features a timeline rail on the left to show distinct actions over time and uses rich rendering (like compact tables and bolded headers) to display information clearly.

What does it allow users to do?

The Logs tab provides transparency and detailed insight into the application's execution, specifically enabling users to:

• Spot Check Data and Debug Issues: Users can see the sequence of data transformations from the source to the final chart, including previews of the dataset at various steps. This allows them to quickly spot check intermediate results and pinpoint where an error or unexpected result might originate.

• Identify Performance Bottlenecks: The UI displays timestamps and time deltas between log messages, allowing users to easily see which parts of the code take longer to execute.

• Gain Code Comfort and Trust in AI: By clearly illustrating that the AI's role is writing code (and the code is doing the data analysis), the logs reduce the perception of a "black box." The logs help users become more comfortable with code-based analytics without requiring them to write the code themselves.

• Utilize Custom and AI-Assisted Debugging:

  • Custom Debugging: Users can request the AI to insert custom logger.debug statements in the code (e.g., to display the top 10 results) without affecting the final rendered chart.

  • AI-Assisted Debugging: The system uses log outputs to inform its AI auto-correction loop, which may insert its own logger.debug statements when attempting to patch faulty code, resulting in better fixes.

• Analyze Errors: When an error occurs, the tab displays rich tracebacks, including the error, the traceback, and the log messages immediately preceding it to provide additional context for the failure.