/advanced-evaluation

name: advanced-evaluation
description: "This skill should be used for advanced LLM evaluation: LLM-as-judge systems, direct scoring, pairwise comparison, rubric calibration, evaluator bias mitigation, confidence scoring, and automated quality assessment."

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 LLM-as-judge systems 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 specifically for LLM or human/LLM hybrid judges
• Analyzing correlation between automated and human judgments

Do not activate this skill for adjacent work owned by other skills:

• General deterministic checks, regression suites, production quality gates, or outcome metrics: `evaluation`.
• Autonomous loop governance, locked rubrics, rollback, or PR approval boundaries: `harness-engineering`.
• Tool API contracts for evaluation tools: `tool-design`.

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. 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.

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.

Require evidence before the score in scoring prompts so the judge must anchor its decision in observable output features before emitting a number.

Pairwise Comparison Implementation

Apply position bias mitigation in every pairwise evaluation:

1. Run deterministic pre-checks first: both candidates must satisfy the same schema, source-evidence requirements, and scope constraints. 2. First judge pass: Response A in first position, Response B in second. 3. Second judge pass: Response B in first position, Response A in second. 4. Consistency check: If passes disagree, return TIE with reduced confidence. 5. Final verdict: Consistent winner with averaged confidence and explicit tie-breaker rationale.

**Prompt Structure for Pairwise Comparison**:

You are an expert evaluator comparing two AI responses.

## Critical Instructions
- Do NOT prefer responses because they are longer
-