Aligning LLM-as-a-Judge with Human Preferences

LangSmith introduces a self-improving LLM-as-a-Judge system that incorporates human feedback to refine evaluation prompts over time, enhancing the accuracy of judging natural language outputs without manual prompt engineering. This approach addresses the challenge of evaluating generative LLM outputs by adapting to user preferences through few-shot learning.