13 个 Skill
# Advanced Evaluation
This skill covers production-grade techniques for evaluating LLM outputs using LLMs as judges. It synthesizes research from academic papers, industry practices, and practical implementation experience into actionable patterns for building reliable evaluation systems.
**Key insight**: LLM-as-a-Judge is not a single technique but a family of approaches, each suited to different evaluation contexts. Choosing the right approach and mitigating known biases is the core competency this skill develops.
## When to Activate
Activate this skill when:
- Building automated evaluation pipelines for LLM outputs
- Comparing multiple model responses to select the best one
- Establishing consistent quality standards across evaluation teams
- Debugging evaluation systems that show inconsistent results
- Designing A/B tests for prompt or model changes
- Creating rubrics for human or automated evaluation
- Analyzing correlation between automated and human judgments
## Core Concepts
### The Evaluation Taxonomy
Select between two primary approaches based on whether ground truth exists:
**Direct Scoring** — Use when objective criteria exist (factual accuracy, instruction following, toxicity). A single LLM rates one response on a defined scale. Achieves moderate-to-high reliability for well-defined criteria. Watch for score calibration drift and inconsistent scale interpretation.
**Pairwise Comparison** — Use for subjective preferences (tone, style, persuasiveness). An LLM compares two responses and selects the better one. Achieves higher human-judge agreement than direct scoring for preference tasks (Zheng et al., 2023). Watch for position bias and length bias.
### The Bias Landscape
Mitigate these systematic biases in every evaluation system:
**Position Bias**: First-position responses get preferential treatment. Mitigate by evaluating twice with swapped positions, then apply majority vote or consistency check.
**Length Bias**: Longer responses score higher regardless of quality. Mitigate by explicitly prompting to ignore length and applying length-normalized scoring.
**Self-Enhancement Bias**: Models rate their own outputs higher. Mitigate by using different models for generation and evaluation.
**Verbosity Bias**: Excessive detail scores higher even when unnecessary. Mitigate with criteria-specific rubrics that penalize irrelevant detail.
**Authority Bias**: Confident tone scores higher regardless of accuracy. Mitigate by requiring evidence citation and adding a fact-checking layer.
### Metric Selection Framework
Match metrics to the evaluation task structure:
| Task Type | Primary Metrics | Secondary Metrics |
|-----------|-----------------|-------------------|
| Binary classification (pass/fail) | Recall, Precision, F1 | Cohen's kappa |
| Ordinal scale (1-5 rating) | Spearman's rho, Kendall's tau | Cohen's kappa (weighted) |
| Pairwise preference | Agreement rate, Position consistency | Confidence calibration |
| Multi-label | Macro-F1, Micro-F1 | Per-label precision/recall |
Prioritize systematic disagreement patterns over absolute agreement rates because a judge that consistently disagrees with humans on specific criteria is more problematic than one with random noise.
## Evaluation Approaches
### Direct Scoring Implementation
Build direct scoring with three components: clear criteria, a calibrated scale, and structured output format.
**Criteria Definition Pattern**:
```
Criterion: [Name]
Description: [What this criterion measures]
Weight: [Relative importance, 0-1]
```
**Scale Calibration** — Choose scale granularity based on rubric detail:
- 1-3: Binary with neutral option, lowest cognitive load
- 1-5: Standard Likert, best balance of granularity and reliability
- 1-10: Use only with detailed per-level rubrics because calibration is harder
**Prompt Structure for Direct Scoring**:
```
You are an expert evaluator assessing response quality.
## Task
Evaluate the following response against each criterion.
## Original Prompt
{prompt}
## Response to Evaluate
{response}
## Criteria
{for each criterion: name, description, weight}
## Instructions
For each criterion:
1. Find specific evidence in the response
2. Score according to the rubric (1-{max} scale)
3. Justify your score with evidence
4. Suggest one specific improvement
## Output Format
Respond with structured JSON containing scores, justifications, and summary.
```
Always require justification before the score in all scoring prompts because research shows this improves reliability by 15-25% compared to score-first approaches.
### Pairwise Comparison Implementation
Apply position bias mitigation in every pairwise evaluation:
1. First pass: Response A in first position, Response B in second
2. Second pass: Response B in first position, Response A in second
3. Consistency check: If passes disagree, return TIE with reduced confidence
4. Final verdict: Consistent winner with averaged confidence
**Prompt Structure for Pairwise Comparison**:
```
You are an expert evaluator comparing two AI responses.
## Critical Instructions
- Do NOT prefer responses because they are longer
- Do NOT prefer responses based on position (first vs second)
- Focus ONLY on quality according to the specified criteria
- Ties are acceptable when responses are genuinely equivalent
## Original Prompt
{prompt}
## Response A
{response_a}
## Response B
{response_b}
## Comparison Criteria
{criteria list}
## Instructions
1. Analyze each response independently first
2. Compare them on each criterion
3. Determine overall winner with confidence level
## Output Format
JSON with per-criterion comparison, overall winner, confidence (0-1), and reasoning.
```
**Confidence Calibration** — Map confidence to position consistency:
- Both passes agree: confidence = average of individual confidences
- Passes disagree: confidence = 0.5, verdict = TIE
### Rubric Generation
Generate rubrics to reduce evaluation variance by 40-60% compared to open-ended scoring.
**Include these rubric components**:
1. **Level descriptions**: Clear boundaries for each score level
2. **Characteristics**: Observable features that define each level
3. **Examples**: Representative text for each level (optional but valuable)
4. **Edge cases**: Guidance for ambiguous situations
5. **Scoring guidelines**: General principles for consistent application
**Set strictness calibration** for the use case:
- **Lenient**: Lower passing bar, appropriate for encouraging iteration
- **Balanced**: Typical production expectations
- **Strict**: High standards for safety-critical or high-stakes evaluation
Adapt rubrics to the domain — use domain-specific terminology. A code readability rubric mentions variables, functions, and comments. A medical accuracy rubric references clinical terminology and evidence standards.
## Practical Guidance
### Evaluation Pipeline Design
Build production evaluation systems with these layers: Criteria Loader (rubrics + weights) -> Primary Scorer (direct or pairwise) -> Bias Mitigation (position swap, etc.) -> Confidence Scoring (calibration) -> Output (scores + justifications + confidence). See [Evaluation Pipeline Diagram](./references/evaluation-pipeline.md) for the full visual layout.
### Decision Framework: Direct vs. Pairwise
Apply this decision tree:
```
Is there an objective ground truth?
+-- Yes -> Direct Scoring
| Examples: factual accuracy, instruction following, format compliance
|
+-- No -> Is it a preference or quality judgment?
+-- Yes -> Pairwise Comparison
| Examples: tone, style, persuasiveness, creativity
|
+-- No -> Consider reference-based evaluation
Examples: summarization (compare to source), translation (compare to reference)
```
### Scaling Evaluation
For high-volume evaluation, apply one of these strategies:
1. **Panel of LLMs (PoLL)**: Use multiple models as judges and aggregate votes to reduce individual model bias. More expensive but more reliable for high-stakes decisions.
2. **Hierarchical evaluation**: Use a fast cheap model for screening and an expensive model for edge cases. Requires calibration of the screening threshold.
3. **Human-in-the-loop**: Automate clear cases and route low-confidence decisions to human review. Design feedback loops to improve automated evaluation over time.
## Examples
### Example 1: Direct Scoring for Accuracy
**Input**:
```
Prompt: "What causes seasons on Earth?"
Response: "Seasons are caused by Earth's tilted axis. As Earth orbits the Sun,
different hemispheres receive more direct sunlight at different times of year."
Criterion: Factual Accuracy (weight: 1.0)
Scale: 1-5
```
**Output**:
```json
{
"criterion": "Factual Accuracy",
"score": 5,
"evidence": [
"Correctly identifies axial tilt as primary cause",
"Correctly explains differential sunlight by hemisphere",
"No factual errors present"
],
"justification": "Response accurately explains the cause of seasons with correct
scientific reasoning. Both the axial tilt and its effect on sunlight distribution
are correctly described.",
"improvement": "Could add the specific tilt angle (23.5 degrees) for completeness."
}
```
### Example 2: Pairwise Comparison with Position Swap
**Input**:
```
Prompt: "Explain machine learning to a beginner"
Response A: [Technical explanation with jargon]
Response B: [Simple analogy-based explanation]
Criteria: ["clarity", "accessibility"]
```
**First Pass (A first)**:
```json
{ "winner": "B", "confidence": 0.8 }
```
**Second Pass (B first)**:
```json
{ "winner": "A", "confidence": 0.6 }
```
(Note: Winner is A because B was in first position)
**Mapped Second Pass**:
```json
{ "winner": "B", "confidence": 0.6 }
```
**Final Result**:
```json
{
"winner": "B",
"confidence": 0.7,
"positionConsistency": {
"consistent": true,
"firstPassWinner": "B",
"secondPassWinner": "B"
}
}
```
### Example 3: Rubric Generation
**Input**:
```
criterionName: "Code Readability"
criterionDescription: "How easy the code is to understand and maintain"
domain: "software engineering"
scale: "1-5"
strictness: "balanced"
```
**Output** (abbreviated):
```json
{
"levels": [
{
"score": 1,
"label": "Poor",
"description": "Code is difficult to understand without significant effort",
"characteristics": [
"No meaningful variable or function names",
"No comments or documentation",
"Deeply nested or convoluted logic"
]
},
{
"score": 3,
"label": "Adequate",
"description": "Code is understandable with some effort",
"characteristics": [
"Most variables have meaningful names",
"Basic comments present for complex sections",
"Logic is followable but could be cleaner"
]
},
{
"score": 5,
"label": "Excellent",
"description": "Code is immediately clear and maintainable",
"characteristics": [
"All names are descriptive and consistent",
"Comprehensive documentation",
"Clean, modular structure"
]
}
],
"edgeCases": [
{
"situation": "Code is well-structured but uses domain-specific abbreviations",
"guidance": "Score based on readability for domain experts, not general audience"
}
]
}
```
## Guidelines
1. **Always require justification before scores** - Chain-of-thought prompting improves reliability by 15-25%
2. **Always swap positions in pairwise comparison** - Single-pass comparison is corrupted by position bias
3. **Match scale granularity to rubric specificity** - Don't use 1-10 without detailed level descriptions
4. **Separate objective and subjective criteria** - Use direct scoring for objective, pairwise for subjective
5. **Include confidence scores** - Calibrate to position consistency and evidence strength
6. **Define edge cases explicitly** - Ambiguous situations cause the most evaluation variance
7. **Use domain-specific rubrics** - Generic rubrics produce generic (less useful) evaluations
8. **Validate against human judgments** - Automated evaluation is only valuable if it correlates with human assessment
9. **Monitor for systematic bias** - Track disagreement patterns by criterion, response type, model
10. **Design for iteration** - Evaluation systems improve with feedback loops
## Gotchas
1. **Scoring without justification**: Scores lack grounding and are difficult to debug. Always require evidence-based justification before the score.
2. **Single-pass pairwise comparison**: Position bias corrupts results when positions are not swapped. Always evaluate twice with swapped positions and check consistency.
3. **Overloaded criteria**: Criteria that measure multiple things at once produce unreliable scores. Enforce one criterion = one measurable aspect.
4. **Missing edge case guidance**: Evaluators handle ambiguous cases inconsistently without explicit instructions. Include edge cases in rubrics with clear resolution rules.
5. **Ignoring confidence calibration**: High-confidence wrong judgments are worse than low-confidence ones. Calibrate confidence to position consistency and evidence strength.
6. **Rubric drift**: Rubrics become miscalibrated as quality standards evolve or model capabilities improve. Schedule periodic rubric reviews and re-anchor score levels against fresh human-annotated examples.
7. **Evaluation prompt sensitivity**: Minor wording changes in evaluation prompts (e.g., reordering instructions, changing phrasing) can cause 10-20% score swings. Version-control evaluation prompts and run regression tests before deploying prompt changes.
8. **Uncontrolled length bias**: Longer responses systematically score higher even when conciseness is preferred. Add explicit length-neutrality instructions to evaluation prompts and validate with length-controlled test pairs.
## Integration
This skill integrates with:
- **context-fundamentals** - Evaluation prompts require effective context structure
- **tool-design** - Evaluation tools need proper schemas and error handling
- **context-optimization** - Evaluation prompts can be optimized for token efficiency
- **evaluation** (foundational) - This skill extends the foundational evaluation concepts
## References
Internal reference:
- [LLM-as-Judge Implementation Patterns](./references/implementation-patterns.md) - Read when: building an evaluation pipeline from scratch or integrating LLM judges into CI/CD
- [Bias Mitigation Techniques](./references/bias-mitigation.md) - Read when: evaluation results show inconsistent or suspicious scoring patterns
- [Metric Selection Guide](./references/metrics-guide.md) - Read when: choosing statistical metrics to validate evaluation reliability
- [Evaluation Pipeline Diagram](./references/evaluation-pipeline.md) - Read when: designing the architecture of a multi-stage evaluation system
External research:
- [Eugene Yan: Evaluating the Effectiveness of LLM-Evaluators](https://eugeneyan.com/writing/llm-evaluators/) - Read when: surveying the state of the art in LLM evaluation
- [Judging LLM-as-a-Judge (Zheng et al., 2023)](https://arxiv.org/abs/2306.05685) - Read when: understanding position bias and MT-Bench methodology
- [G-Eval: NLG Evaluation using GPT-4 (Liu et al., 2023)](https://arxiv.org/abs/2303.16634) - Read when: implementing chain-of-thought evaluation scoring
- [Large Language Models are not Fair Evaluators (Wang et al., 2023)](https://arxiv.org/abs/2305.17926) - Read when: diagnosing systematic bias in evaluation outputs
Related skills in this collection:
- evaluation - Foundational evaluation concepts
- context-fundamentals - Context structure for evaluation prompts
- tool-design - Building evaluation tools
---
## Skill Metadata
**Created**: 2025-12-24
**Last Updated**: 2026-03-17
**Author**: Agent Skills for Context Engineering Contributors
**Version**: 2.0.0