advanced-evaluation
Master LLM-as-a-Judge evaluation techniques including direct scoring, pairwise comparison, rubric generation, and bias mitigation. Use when building evaluation systems, comparing model outputs, or establishing quality standards for AI-generated content.
What this skill does
# Advanced Evaluation LLM-as-a-Judge techniques for evaluating AI outputs. Not a single technique but a family of approaches - choosing the right one and mitigating biases is the core competency. ## When to Activate - Building automated evaluation pipelines for LLM outputs - Comparing multiple model responses to select the best one - Establishing consistent quality standards - Debugging inconsistent evaluation results - Designing A/B tests for prompt or model changes - Creating rubrics for human or automated evaluation ## Core Concepts ### Evaluation Taxonomy **Direct Scoring**: Single LLM rates one response on a defined scale. - Best for: Objective criteria (factual accuracy, instruction following, toxicity) - Reliability: Moderate to high for well-defined criteria **Pairwise Comparison**: LLM compares two responses and selects better one. - Best for: Subjective preferences (tone, style, persuasiveness) - Reliability: Higher than direct scoring for preferences ### Known Biases | Bias | Description | Mitigation | |------|-------------|------------| | Position | First-position preference | Swap positions, check consistency | | Length | Longer = higher scores | Explicit prompting, length-normalized scoring | | Self-Enhancement | Models rate own outputs higher | Use different model for evaluation | | Verbosity | Unnecessary detail rated higher | Criteria-specific rubrics | | Authority | Confident tone rated higher | Require evidence citation | ### Decision Framework ``` Is there an objective ground truth? ├── Yes → Direct Scoring (factual accuracy, format compliance) └── No → Pairwise Comparison (tone, style, creativity) ``` ## Quick Reference ### Direct Scoring Requirements 1. Clear criteria definitions 2. Calibrated scale (1-5 recommended) 3. Chain-of-thought: justification BEFORE score (improves reliability 15-25%) ### Pairwise Comparison Protocol 1. First pass: A in first position 2. Second pass: B in first position (swap) 3. Consistency check: If passes disagree → TIE 4. Final verdict: Consistent winner with averaged confidence ### Rubric Components - Level descriptions with clear boundaries - Observable characteristics per level - Edge case guidance - Strictness calibration (lenient/balanced/strict) ## Integration Works with: - **context-fundamentals** - Effective context structure - **tool-design** - Evaluation tool schemas - **evaluation** (foundational) - Core evaluation concepts --- **For detailed implementation patterns, prompt templates, examples, and metrics:** `references/full-guide.md` See also: `references/implementation-patterns.md`, `references/bias-mitigation.md`, `references/metrics-guide.md`
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