Implements comprehensive LLM output evaluation workflows including manual feedback, automated scoring functions, and AI-assisted quality assessment.
This skill provides a standardized framework for measuring and improving AI application performance using Langfuse. It enables developers to integrate user feedback mechanisms, define automated evaluation metrics (such as topic coverage and response length), and implement 'LLM-as-judge' patterns for objective quality scoring. By automating the comparison of different prompt versions through A/B testing and score normalization, it helps engineering teams move from subjective assessments to data-driven optimization of their AI workflows and models.
주요 기능
01Manual user feedback and star rating integration
02A/B testing framework to compare performance between prompt versions
03Score normalization and dataset building for continuous improvement
04Automated quality scoring for topic coverage and response constraints
050 GitHub stars
06LLM-as-judge implementation for complex qualitative evaluations
사용 사례
01Gathering and structured user feedback to build fine-tuning datasets
02Comparing the effectiveness of two different prompt engineering strategies
03Setting up automated quality gates for production LLM deployments
