Who judges the judge? Three helpful ways to audit your LLM evaluators

Is your LLM-as-a-judge truly objective? There’s a common misconception in tech that humans are subjective, so we should rely on AI for objective evaluation. But the reality is that LLMs are also subjective. Just like human reviewers, they can carry biases: Verbosity bias: LLM judges usually favor longer, confident-sounding answers, even when the underlying facts are wrong. Position bias: Swapping the order of two options can cause an LLM judge to flip its verdict over 75% of the time. The halo effect: Because LLMs generate text piece by piece, clean grammar and formatting can trick an AI judge