This article outlines a method for building robust evaluation pipelines for Large Language Models (LLMs). It emphasizes the use of "Golden Datasets" and the "LLM-as-a-Judge" technique to assess model performance. The proposed approach integrates these evaluations into continuous integration (CI) processes through "Regression Gates" to catch performance degradations before they impact production. AI
IMPACT Provides a practical framework for improving LLM quality control and deployment reliability.
RANK_REASON The article describes a methodology for evaluating LLMs within a MLOps context, which is a tool-oriented application of AI.
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