Scoring

Automated scoring applies predefined criteria to evaluate inputs and assigns a numerical score alongside a confidence level. It outputs structured JSON detailing the score, reasoning, and confidence percentage to ensure evaluation consistency.

Evaluating research findings quality is best handled by applying a consistent scoring rubric to assess the information. This objective evaluation method ensures consistent measurement and reduces manual effort across multiple datasets.

Yes, you can get structured JSON output when scoring document relevance. The evaluation process details the assigned score, the reasoning behind it, and a quantifiable confidence percentage directly within the JSON structure.

Confidence assessment handles edge cases like insufficient data or conflicting criteria by adjusting the evaluation output. It provides a quantifiable measure of certainty that reflects data limitations rather than failing the process.

Yes, you need to define evaluation criteria before scoring dataset quality. The scoring mechanism applies a consistent rubric based on your predefined parameters to assign objective scores and confidence levels.