Automated evaluation using LLM-as-Judge offers significant practical benefits for industrial applications. However, the commonly recognized misalignment of judgment biases between humans and LLM-as-Judge hinders its usage in real-world businesses. Although preference-finetuning could be a potential solution, it is often impractical for industrial use-cases due to the scarcity of business-specific data and the infeasibility of applying it to closed models. In this paper, we propose InstaJudge, an LLM-as-Judge library that improves alignments of judgment biases through automatic prompt optimization (APO). Our library not only integrates recent APO methods within a unified framework but also introduces a novel APO approach called distribution-preserving few-shot sampling (DPFS). Experimental results verify demonstrate DPFS significantly outperforms existing LLM-as-Judge libraries, like DeepEval, and APO methods by a large margin, while being more cost efficient.

Data drift, which denotes a misalignment between the distribution of reference (i.e., training) and production data, constitutes a significant challenge for AI applications, as it undermines the generalisation capacity of machine learning (ML) models. Therefore, it is imperative to proactively identify data drift before users meet with performance degradation. Moreover, to ensure the successful execution of AI services, endeavours should be directed not only toward detecting the occurrence of drift but also toward effectively addressing this challenge. % considering the limited resources prevalent in practical industrial domains. In this work, we introduce a tool designed to detect data drift in text data. In addition, we propose an unsupervised sampling technique for extracting representative examples from drifted instances. This approach bestows a practical advantage by significantly reducing expenses associated with annotating the labels for drifted instances, an essential prerequisite for retraining the model to sustain its performance on production data.