Company
Date Published
Author
By LangChain
Word count
1214
Language
English
Hacker News points
1

Summary

LLM (Large Language Model) evaluations are crucial for improving the performance of these models, but they pose challenges in measuring their outputs programmatically due to the lack of good metrics. To address this issue, an "LLM-as-a-Judge" approach is used, where a separate LLM is passed the generated output and asked to judge it. However, this raises the problem of prompt engineering for the evaluator prompt, which can be time-consuming. LangSmith presents a novel solution to this problem by implementing "self-improving" evaluators that utilize human corrections as few-shot examples, which are then fed back into the prompt in future iterations. This approach aims to streamline the evaluation process and align LLM evaluations with human preferences, eliminating manual prompt adjustments or time-consuming prompt engineering. LangSmith's self-improving evaluators provide an elegant solution to this problem, leveraging few-shot learning and user corrections to integrate human feedback for accurate, relevant evaluations without constant manual intervention.