Multi-dimensional model output quality assessment program
Langfuse builds a hybrid evaluation system that supports both manual labeling of output quality in the web interface (on a 0-1 scale) and provides an API interface for automated scoring (langfuse.score method). Evaluation dimensions include not only traditional factual accuracy, but also customizable business-specific metrics such as relevance and fluency.
In terms of technical implementation, the scoring data maintains a strong correlation with the original trace records, supporting the analysis of model performance trends in the time dimension. The platform also uniquely supports immediate debugging by jumping directly from the error tracing results to Playground, forming a complete closed-loop workflow of "observation-assessment-optimization". This design significantly shortens the model iteration cycle.
This answer comes from the articleLangfuse: an open source LLM application observation and debugging platformThe
