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Maxim AI’s LLM as a Judge Implementation

Maxim AI provides robust LLM-as-a-judge evaluation with:
  • Flexible Judge Configuration: Define custom evaluation criteria and judge prompts tailored to your specific requirements.
  • Multi-Model Support: Use different LLMs as judges (GPT-4, Claude, etc.) and compare their assessments.
  • Automated Execution: Run LLM evaluations automatically across your test sets and production traffic.
  • Structured Outputs: Extract and visualize judgment scores, categories, and explanations for easy analysis.
  • Consistency Monitoring: Track how judge assessments perform over time and identify evaluation drift.
LLM as a Judge enables teams to evaluate AI outputs at scale with human-like judgment, making quality assurance practical and continuous.

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Best Practices for LLM as a Judge

  • Start with Clear Criteria: Define exactly what you’re evaluating before designing judge prompts.
  • Validate Against Humans: Compare LLM judgments to human evaluations on a sample set to ensure alignment.
  • Use Stronger Models: More capable models (GPT-4, Claude) generally provide better judgments than smaller models.
  • Implement Quality Checks: Randomly audit LLM judgments to catch systematic errors or drift.
  • Combine Multiple Judges: For critical evaluations, use multiple LLM judges or combine LLM judgment with automated metrics.