advanced-evaluation

# 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
- 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_