Are You Making These 7 LLM-as-a-Judge Mistakes?

Engineering teams often face reliability issues with LLM-based judges, primarily due to inconsistent performance in production environments, with 93% of teams reporting such challenges. These problems stem from how teams implement, maintain, and architect LLM judges rather than the methodology itself. Common mistakes include using numeric scores instead of binary verdicts, relying on single judge opinions, failing to update judge prompts over time, and using general-purpose models for specific evaluation tasks. Effective solutions include asking binary questions, employing multiple smaller judges for consensus, continuously updating judge prompts based on real-world data, and using specialized models for cost-efficient, accurate evaluations. Further, teams are encouraged to track system-wide behavior to prevent compound errors and to optimize evaluation costs to achieve comprehensive coverage. Galileo offers tools and methodologies to help teams build consistent, scalable evaluation infrastructures by addressing these issues with approaches like binary question frameworks, multi-headed architecture, and continuous prompt optimization.