Llm As Judge
Use when validating subjective quality criteria that cannot be deterministically tested — applies…
by pixel-process-ug · MIT
The skill
Use when validating subjective quality criteria that cannot be deterministically tested — applies LLM-based evaluation with structured rubrics for tone, aesthetics, UX feel, documentation quality, and code readability. Triggers: documentation quality check, error message tone review, UX copy evaluation, code readability assessment, design aesthetic review.
LLM-as-Judge
Overview
Some quality criteria are inherently subjective — tone of voice, visual aesthetics, UX feel, documentation clarity, code readability. These cannot be verified by deterministic tests. The LLM-as-judge pattern provides structured, repeatable evaluation using an LLM reviewer with defined rubrics, ensuring subjective quality is measured consistently.
Announce at start: "I'm using the llm-as-judge skill to evaluate subjective quality."
Phase 1: Determine Evaluation Method
Goal: Decide whether LLM-as-judge is the right tool.
Decision Table: LLM-as-Judge vs Deterministic Tests
| Criterion Type | Method | Example |
|---|---|---|
| Objective, measurable | Deterministic test | "Response time < 200ms" |
| Structural, verifiable | Deterministic test | "Returns valid JSON" |
| Subjective, qualitative | LLM-as-judge | "Error messages are friendly and helpful" |
| Aesthetic, perceptual | LLM-as-judge | "UI feels clean and modern" |
| Linguistic, tonal | LLM-as-judge | "Documentation is clear for beginners" |
| Holistic, experiential | LLM-as-judge | "The onboarding flow feels intuitive" |
Rule of thumb: If you can write a boolean assertion, use a deterministic test. If evaluation requires judgment, use LLM-as-judge.
STOP — Do NOT proceed to Phase 2 until:
- [ ] Confirmed that criteria are genuinely subjective
- [ ] Deterministic testing has been ruled out
- [ ] Specific artifacts to evaluate are identified
Phase 2: Define Rubric
Goal: Create evaluation dimensions with weights and anchor points.
Actions
- Define 3-5 evaluation dimensions
- Assign weights (must sum to 1.0)
- Define anchor points for each dimension (1=worst, 5=adequate, 10=best)
- Set passing threshold
Rubric Structure
| Dimension | Weight | Scale | Anchor: 1 | Anchor: 5 | Anchor: 10 |
|---|---|---|---|---|---|
| [Name] | 0.X | 1-10 | [Worst case] | [Adequate] | [Excellent] |
Threshold Selection Table
| Quality Level | Threshold | Use For |
|---|---|---|
| Minimum viable | 5.0 | Internal docs, draft content |
| Production quality | 7.0 | User-facing content, public APIs |
| Excellence | 8.5 | Marketing, critical UX flows |
STOP — Do NOT proceed to Phase 3 until:
- [ ] 3-5 dimensions are defined with clear descriptions
- [ ] Weights sum to exactly 1.0
- [ ] Anchor points are specific (not vague)
- [ ] Passing threshold is set before evaluation
Phase 3: Evaluate
Goal: Submit the artifact with rubric to an LLM reviewer.
Review Request Structure
{
criteria: "Description of what to evaluate and the quality standard",
artifact: "The content to be evaluated (code, text, UI markup, etc.)",
rubric: [
{ dimension: "Clarity", weight: 0.3, description: "Is the content easy to understand?" },
{ dimension: "Tone", weight: 0.3, description: "Is the tone appropriate for the audience?" },
{ dimension: "Completeness", weight: 0.2, description: "Does it cover all necessary points?" },
{ dimension: "Engagement", weight: 0.2, description: "Does it hold the reader's interest?" }
],
passing_threshold: 7.0,
intelligence: "opus"
}
Review Response Structure
{
scores: [
{ dimension: "Clarity", score: 8, reasoning: "Well-structured with clear headings..." },
{ dimension: "Tone", score: 7, reasoning: "Professional but occasionally too formal..." },
{ dimension: "Completeness", score: 9, reasoning: "Covers all key topics..." },
{ dimension: "Engagement", score: 6, reasoning: "Could use more examples..." }
],
weighted_score: 7.4,
pass: true,
summary: "Overall good quality with minor tone and engagement improvements suggested.",
suggestions: [
"Add a real-world example in section 3",
"Use more conversational language in the introduction"
]
}
STOP — Do NOT proceed to Phase 4 until:
- [ ] Artifact has been submitted with full rubric
- [ ] Each dimension has been scored independently
- [ ] Reasoning is provided for every score
- [ ] Weighted total is calculated
Phase 4: Iterate or Accept
Goal: Act on the evaluation results.
Result Action Table
| Result | Action | Max Iterations |
|---|---|---|
| Pass (score >= threshold) | Accept the artifact, proceed | Done |
| Marginal fail (within 1 point) | Apply suggestions, re-evaluate once | 1 |
| Clear fail (> 1 point below) | Significant revision, apply all suggestions | 2 |
| Repeated fail (3+ attempts) | Escalate — rubric or approach may need adjustment | Escalate |
STOP — Evaluation complete when:
- [ ] Artifact passes threshold, OR
- [ ] 3 iterations completed and escalation decision made
Common Rubric Templates
Documentation Quality
Clarity (0.3): Is the content easy to understand for the target audience?
1=incomprehensible 5=adequate but requires re-reading 10=crystal clear
Accuracy (0.3): Is the information technically correct?
1=factually wrong 5=mostly correct 10=perfectly accurate
Completeness (0.2): Does it cover all necessary topics?
1=missing critical info 5=covers basics 10=comprehensive
Examples (0.2): Are there sufficient, relevant code examples?
1=no examples 5=some examples 10=rich, varied examples
Threshold: 7.0
Error Message Quality
Helpfulness (0.4): Does the message help the user fix the problem?
1=no help at all 5=vague direction 10=exact fix steps
Clarity (0.3): Is the message easy to understand?
1=cryptic 5=understandable 10=immediately clear
Tone (0.2): Is the tone empathetic and non-blaming?
1=hostile/blaming 5=neutral 10=empathetic and supportive
Actionability (0.1): Does it suggest a concrete next step?
1=no suggestion 5=vague suggestion 10=specific actionable step
Threshold: 7.5
Code Readability
Naming (0.3): Are variable/function names descriptive and consistent?
1=single letters everywhere 5=adequate 10=self-documenting
Structure (0.3): Is the code logically organized?
1=spaghetti 5=functional 10=elegant and clear
Simplicity (0.2): Is the code as simple as possible (but not simpler)?
1=over-engineered 5=reasonable 10=minimal and clear
Documentation (0.2): Are complex sections adequately commented?
1=no comments where needed 5=some comments 10=well-documented why
Threshold: 7.0
UX Copy
Clarity (0.3): Is the copy easy to understand?
1=confusing 5=understandable 10=immediately clear
Brevity (0.2): Is it concise without losing meaning?
1=verbose 5=adequate length 10=perfectly concise
Tone (0.2): Does it match the brand voice?
1=off-brand 5=neutral 10=perfectly on-brand
Actionability (0.2): Do CTAs clearly communicate what happens next?
1=unclear 5=adequate 10=crystal clear action
Accessibility (0.1): Is the language inclusive and jargon-free?
1=exclusionary 5=neutral 10=fully inclusive
Threshold: 7.5
Anti-Patterns / Common Mistakes
| Anti-Pattern | Why It Is Wrong | Correct Approach |
|---|---|---|
| Using LLM-as-judge for measurable criteria | Wastes tokens, less reliable than assertions | Use deterministic tests for anything quantifiable |
| Vague rubric dimensions ("is it good?") | Produces unreliable, inconsistent scores | Specific dimensions with anchored examples |
| No passing threshold defined | No way to determine pass/fail objectively | Always set threshold before evaluation |
| Adjusting rubric to pass failing content | Defeats the purpose of quality gates | Fix the content, not the rubric |
| Single evaluation without reasoning | Cannot improve without understanding why | Always require per-dimension reasoning |
| Using weaker model for evaluation | Lower quality judgments | Use strongest available model (Opus) |
| Skipping re-evaluation after changes | No verification that changes improved quality | Always re-evaluate after revisions |
Integration Points
| Skill | Relationship |
|---|---|
acceptance-testing |
LLM-as-judge handles subjective acceptance criteria |
spec-writing |
Specs may include subjective quality criteria |
code-review |
Readability evaluation during code review |
verification-before-completion |
Subjective validation gate before completion |
senior-prompt-engineer |
Prompt quality evaluation uses LLM-as-judge |
tech-docs-generator |
Documentation quality evaluation |
Downstream Steering Pattern
+----------+ +----------+ +----------+ +----------+
| SPECS |---->| CODE |---->| TESTS |---->| LLM-AS- |
| | | | |(determin)| | JUDGE |
| | | | | | |(subject) |
+----------+ +----------+ +----------+ +----+-----+
^ |
| backpressure |
+----------------------------------+
Deterministic tests validate objective criteria. LLM-as-judge validates subjective criteria. Both must pass.
Skill Type
FLEXIBLE — Adapt rubric dimensions and thresholds to context. The pattern structure (define rubric, evaluate, score, iterate) is fixed. Always set the threshold before evaluation, never after.
Use this skill
Connect your agent to the Skill Store MCP server and ask for it by ref: pixel-process-ug/llm-as-judge-68528075204.
Details
- Publisher
- pixel-process-ug
- Category
- Software Development › Testing & QA
- License
- MIT
- Updated
- 2026-03-16
References
More from pixel-process-ug
- Acceptance Testing Use when validating that implementation meets specification requirements — applies acceptance-dri…
- Agent Development Use when the user needs to build AI agents — tool use patterns, memory management, planning strat…
- Api Design Use when designing API endpoints, defining request/response schemas, generating OpenAPI specifica…
- Artifacts Builder Use when the user needs standalone HTML/CSS/JS artifacts — interactive demos, prototypes, single-…