This skill should be used when the user asks to "implement LLM-as-judge", "compare model outputs", "create evaluation rubrics", "mitigate evaluation bias", or mentions direct scoring, pairwise comparison, position bias, evaluation pipelines, or automated quality assessment.
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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.
Activate this skill when:
Do not activate this skill for adjacent work owned by other skills:
evaluation.harness-engineering.tool-design.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. Pairwise methods often correlate better with human preference than open-ended direct scoring for subjective tasks (claim-advanced-evaluation-position-swap). Watch for position bias and length bias.
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.
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.
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:
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.
Require evidence before the score in scoring prompts so the judge must anchor its decision in observable output features before emitting a number.
Apply position bias mitigation in every pairwise evaluation:
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:
Generate rubrics to reduce evaluation variance compared to open-ended scoring. Treat exact variance reduction as workload-specific unless measured on the target eval set.
Include these rubric components:
Set strictness calibration for the use case:
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.
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 for the full visual layout.
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)
For high-volume evaluation, apply one of these strategies:
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.
Hierarchical evaluation: Use a fast cheap model for screening and an expensive model for edge cases. Requires calibration of the screening threshold.
Human-in-the-loop: Automate clear cases and route low-confidence decisions to human review. Design feedback loops to improve automated evaluation over time.
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:
{
"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."
}
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):
{ "winner": "B", "confidence": 0.8 }
Second Pass (B first):
{ "winner": "A", "confidence": 0.6 }
(Note: Winner is A because B was in first position)
Mapped Second Pass:
{ "winner": "B", "confidence": 0.6 }
Final Result:
{
"winner": "B",
"confidence": 0.7,
"positionConsistency": {
"consistent": true,
"firstPassWinner": "B",
"secondPassWinner": "B"
}
}
Input:
criterionName: "Code Readability"
criterionDescription: "How easy the code is to understand and maintain"
domain: "software engineering"
scale: "1-5"
strictness: "balanced"
Output (abbreviated):
{
"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"
}
]
}
Always require evidence before scores - Evidence-first prompts make judgments easier to audit and reduce ungrounded numeric scoring
Always swap positions in pairwise comparison - Single-pass comparison is corrupted by position bias
Match scale granularity to rubric specificity - Don't use 1-10 without detailed level descriptions
Separate objective and subjective criteria - Use direct scoring for objective, pairwise for subjective
Include confidence scores - Calibrate to position consistency and evidence strength
Define edge cases explicitly - Ambiguous situations cause the most evaluation variance
Use domain-specific rubrics - Generic rubrics produce generic (less useful) evaluations
Validate against human judgments - Automated evaluation is only valuable if it correlates with human assessment
Monitor for systematic bias - Track disagreement patterns by criterion, response type, model
Design for iteration - Evaluation systems improve with feedback loops
Scoring without justification: Scores lack grounding and are difficult to debug. Always require evidence-based justification before the score.
Single-pass pairwise comparison: Position bias corrupts results when positions are not swapped. Always evaluate twice with swapped positions and check consistency.
Overloaded criteria: Criteria that measure multiple things at once produce unreliable scores. Enforce one criterion = one measurable aspect.
Missing edge case guidance: Evaluators handle ambiguous cases inconsistently without explicit instructions. Include edge cases in rubrics with clear resolution rules.
Ignoring confidence calibration: High-confidence wrong judgments are worse than low-confidence ones. Calibrate confidence to position consistency and evidence strength.
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.
Evaluation prompt sensitivity: Minor wording changes in evaluation prompts can cause material score swings. Version-control evaluation prompts and run regression tests before deploying prompt changes.
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.
This skill owns judge design and bias mitigation. Adjacent skills own broader quality gates and infrastructure:
evaluation: general deterministic checks, regression suites, quality gates, and production monitoring.context-fundamentals: context structure for judge prompts.tool-design: schemas and error handling for evaluation tools.context-optimization: token and latency efficiency for high-volume evals.harness-engineering: locked evaluator surfaces and governance for autonomous loops.Internal reference:
External research:
Related skills in this collection:
Created: 2025-12-24 Last Updated: 2026-05-15 Author: Agent Skills for Context Engineering Contributors Version: 2.1.0
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