Measuring and improving the quality of LLM outputs through automated and human feedback.
Use an LLM with a custom scoring rubric to evaluate open-ended outputs at scale, replacing expensive human review with consistent automated grading.
Improve LLM outputs through iterative generate-evaluate-critique-regenerate loops that refine quality without retraining the model.