Aligning LLM-as-a-Judge with Human Preferences
LangSmith introduces a self-improving LLM-as-a-Judge system that eliminates the need for extensive prompt engineering, allowing users to set up evaluators with minimal configuration. The system stores user corrections as few-shot examples, which are used to refine the evaluator's performance over time, enabling it to adapt to human preferences without manual intervention. This approach streamlines evaluation by automatically integrating real-world feedback into the model's training process.
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