A common tech misconception is that AI offers objective evaluations, but LLMs, like humans, are subjective. LLM judges exhibit several biases, including verbosity bias, position bias, and the halo effect, where clear formatting can mislead them. They also show agreeableness bias, failing to detect errors, and self-enhancement bias by favoring their own outputs. Therefore, LLM judges are not objective and treating them as infallible risks compromised work. To improve LLM judge quality, a three-pillar method is suggested. The first pillar is measuring internal stability by checking consistency across multiple passes for identical inputs. The second pillar involves validating human alignment using chance-corrected metrics to ensure they identify real errors, not just agree with lenient scores. The third pillar advocates for using multi-judge ensembles, employing decomposed prompting for specialized scoring and running multiple models simultaneously to track performance and identify divergences. Partnering with Delta can help implement statistically-audited evaluation pipelines.