LLMs as Judges: Practical Problems and How to Avoid Them
Last Updated on September 4, 2025 by Editorial Team
Author(s): Katherine Munro
Originally published on Towards AI.
Concrete advice for teams building LLM-powered evaluations
My last post was all about conceptual problems with using Large Language Models to judge other LLMs.
The article discusses the practical challenges of using Large Language Models (LLMs) as judges in evaluations, highlighting issues such as non-determinism in both the LLMs being evaluated and the evaluators themselves, prompting errors, and biases inherent in LLMs. It emphasizes the importance of human oversight, the complexity of accurately assessing LLM outputs, and the need for comprehensive evaluation metrics to ensure reliable assessments while cautioning against over-reliance on automated evaluations.
Read the full blog for free on Medium.
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