llm-as-judge（LLM AS judge 搜索）

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

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

1. Define 3-5 evaluation dimensions
2. Assign weights (must sum to 1.0)
3. Define anchor points for each dimension (1=worst, 5=adequate, 10=best)
4. Set passing threshold

Rubric Structure

Threshold Selection Table

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

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

Integration Points

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.