Advanced Evaluation (LLM-as-Judge)

High-rigor evaluation framework using LLM-as-judge methodology. Applies systematic bias mitigation, pairwise comparison, and multi-pass scoring to produce reliable, calibrated quality assessments for high-stakes outputs.

When to Use This Skill vs Basic Evaluation

Use advanced-evaluation when:

• Comparing two or more competing outputs (A vs B)
• The output will be used in high-stakes contexts (public launch, investor materials, policy)
• You suspect the evaluation might be influenced by order, framing, or recency bias
• You need confidence intervals, not just point scores
• You want to stress-test an output with adversarial probing

Use evaluation (base skill) for routine quality checks.

Core Methodology

1. Direct Scoring with Rubrics

Apply the base evaluation rubric from the evaluation skill, but with enhanced evidence requirements:

Evidence Quality Standard:

• Level 3 (Required): Quote specific text; explain why it's a problem; link to what "good" looks like
• Level 2 (Acceptable): Specific example without full context
• Level 1 (Insufficient): Generic statement ("the writing is unclear") — NEVER use this

2. Position Bias Mitigation

When evaluating a single output, run TWO passes:

• Pass A: Evaluate normally (top to bottom)
• Pass B: Evaluate in reverse order (bottom to top, or reframe the prompt)
• Reconcile: If scores differ by >0.5, investigate — bias likely; use the lower score

When comparing two outputs (A vs B):

• Score A first, then B — record scores
• Score B first, then A — record scores
• Final score = average of both orderings
• Flag if position bias > 0.5 delta on any dimension

3. Pairwise Comparison Protocol

When comparing N outputs:

#### Round-Robin Comparison

| Match | Winner | Margin | Key Differentiator |
|-------|--------|--------|--------------------|
| A vs B | [A/B/tie] | [0-2] | [specific reason] |
| A vs C | [A/C/tie] | [0-2] | [specific reason] |
| B vs C | [B/C/tie] | [0-2] | [specific reason] |

**Ranking**: [1st] > [2nd] > [3rd]
**Consensus**: [High/Medium/Low — based on margin consistency]

4. Self-Consistency Check

After scoring, ask: "If I saw this output cold without knowing the context, would I give the same scores?"

Run a quick adversarial probe:

• Steelman the output: What's the strongest case for a HIGHER score?
• Devil's advocate: What's the strongest case for a LOWER score?
• Adjust if necessary: If either argument is compelling, revise the score with explanation

5. Calibration Reference

Use these anchors to calibrate your scores against known examples:

Score	Anchor
5	Wikipedia featured article quality; textbook explanation; Paul Graham essay
4	Good Stack Overflow accepted answer; solid technical blog post
3	Average README; generic ChatGPT output; first-draft document
2	Incomplete FAQ; bullet-point notes without synthesis
1	Wrong, misleading, or incoherent

Advanced Scoring Dimensions

In addition to the base dimensions (Accuracy, Completeness, Usefulness, Clarity, Freshness), add:

Dimension	Weight	Criteria
Coherence	bonus	Does the output have internal logical consistency? No contradictions?
Originality	bonus	Does it add genuine insight beyond summarizing?
Calibration	bonus	Are claims appropriately hedged vs stated with false certainty?

These are bonus dimensions — include when relevant, skip when not applicable.

Output Format

## Advanced Evaluation Report

**Date**: [today]
**Output evaluated**: [name/description]
**Methodology**: LLM-as-Judge with position bias mitigation
**Passes**: [1 / 2 / N]

---

### Pass 1 Scores (Forward)

| Dimension | Score | Evidence (Level 3) | Improvement |
|-----------|-------|-------------------|-------------|
| Accuracy | [1-5] | "[exact quote]" — [why this is an issue] | [specific fix] |
| Completeness | [1-5] | "[what's missing]" | [what to add] |
| Usefulness | [1-5] | "[does it achieve goal?]" | [how to improve] |
| Clarity | [1-5] | "[structural/language issues]" | [how to clarify] |
| Freshness | [1-5] | "[stale elements]" | [what to update] |
| **Subtotal** | **[X.X/5]** | | |

### Pass 2 Scores (Reverse / Alternative Framing)

[Same table with reverse-order evaluation]

### Position Bias Check

| Dimension | Pass 1 | Pass 2 | Delta | Bias Detected? |
|-----------|--------|--------|-------|----------------|
| Accuracy | | | | Yes/No |
| ... | | | | |
| **Overall bias impact**: [High/Medium/Low/None] |

### Adversarial Probing

**Steelman (case for higher score)**: [strongest argument]
**Devil's advocate (case for lower score)**: [strongest argument]
**Conclusion**: [revised score if warranted, with reasoning]

---

### Final Calibrated Score

| Dimension | Score | Confidence |
|-----------|-------|------------|
| Accuracy | [1-5] | [High/Medium/Low] |
| Completeness | [1-5] | [H/M/L] |
| Usefulness | [1-5] | [H/M/L] |
| Clarity | [1-5] | [H/M/L] |
| Freshness | [1-5] | [H/M/L] |
| **Weighted Total** | **[X.X/5]** | |
| **Grade** | **[A/B/C/D/F]** | |

### Prioritized Improvements

**MUST FIX** (blocks use):
1. [Specific issue with Level 3 evidence] → [exact fix]

**SHOULD FIX** (significant quality gain):
2. [Issue] → [fix]

**NICE TO HAVE**:
3. [Issue] → [fix]

### Recommendation
[Clear, unambiguous recommendation with reasoning]

Quality Criteria

An advanced evaluation MUST:

• Run at least 2 passes (forward + reverse or alternative framing)
• Document position bias check with explicit delta
• Include adversarial probing (steelman + devil's advocate)
• Provide Level 3 evidence for every score below 4
• State confidence levels alongside scores
• Produce a clear, actionable recommendation

Anti-Patterns

• Anchoring: Letting the first impression lock in the final score — mitigated by Pass 2
• Halo effect: Letting one strong dimension inflate others — each dimension scored independently
• Confirmation bias: Looking for evidence to confirm initial impression — adversarial probing counteracts
• False precision: Giving 3.7/5 when evidence supports 3-4 range — use confidence levels instead