Enhance your scoring system's accuracy by incorporating ratings from trained raters who evaluate model outputs against product guidelines. The system leverages the high-quality human feedback to adjust the scoring tree’s weights and thresholds, resulting in a system that directly correlates with expert judgment of good performance. This approach uses preference data and Likert scale ratings to calibrate the shape of the scoring tree.
While automated metrics provide a foundation for quality assessment, human judgment often captures nuances that automated systems miss. Learning to effectively calibrate scoring systems with rater feedback helps you create more sophisticated and accurate quality metrics while requiring relatively modest amounts of training data – often just hundreds of rated examples rather than thousands.
Implement this technique when you need your scoring system to reflect sophisticated quality judgments that go beyond surface-level metrics. For example, when developing an AI writing assistant for technical documentation, trained raters can help calibrate how the system scores factors like clarity, technical accuracy, and appropriate detail level. This approach is particularly valuable when working in specialized domains where quality assessment requires domain expertise, or when you need to ensure consistent quality standards across different use cases.