mediar-ai/packages/skills/skills/advanced-evaluation

Advanced Evaluation

Production-grade techniques for evaluating LLM outputs using LLM-as-judge approaches with bias mitigation.

Prerequisites

• Understanding of evaluation metrics
• Access to LLM APIs for judge models

Instructions

Core Approaches

Direct Scoring: Single LLM rates one response on a defined scale.

• Best for: Objective criteria (factual accuracy, instruction following)
• Requires: Clear criteria, calibrated scale, chain-of-thought justification

Pairwise Comparison: LLM compares two responses and selects the better one.

• Best for: Subjective preferences (tone, style, persuasiveness)
• Requires: Position bias mitigation (swap positions and check consistency)

Bias Mitigation

| Bias | Mitigation | |------|------------| | Position Bias | Evaluate twice with swapped positions | | Length Bias | Explicit prompting to ignore length | | Self-Enhancement | Use different models for generation and evaluation | | Verbosity Bias | Criteria-specific rubrics |

Pairwise Comparison Protocol

1. First pass: Response A first, Response B second
2. Second pass: Response B first, Response A second
3. Consistency check: If passes disagree, return TIE
4. Final verdict: Consistent winner with averaged confidence

Rubric Components

1. Level descriptions: Clear boundaries for each score
2. Characteristics: Observable features per level
3. Examples: Representative text (optional but valuable)
4. Edge cases: Guidance for ambiguous situations

Decision Framework

Is there objective ground truth?
├── Yes → Direct Scoring (factual accuracy, format compliance)
└── No → Is it preference/quality judgment?
    ├── Yes → Pairwise Comparison (tone, creativity)
    └── No → Reference-based evaluation

Guidelines

1. Always require justification before scores (15-25% reliability improvement)
2. Always swap positions in pairwise comparison
3. Match scale granularity to rubric specificity
4. Separate objective and subjective criteria
5. Include confidence scores calibrated to evidence strength

Notes

• Chain-of-thought prompting improves evaluation reliability
• Single-pass pairwise comparison is corrupted by position bias
• Validate automated evaluation against human judgments

Source: muratcankoylan/Agent-Skills-for-Context-Engineering