output-eval-judge-prompt

Designing LLM Judge Prompts

Overview

An LLM judge evaluates workflow output for a single, specific failure mode identified during error analysis. This skill covers how to design the .prompt file that powers judgeVerdict(), judgeScore(), or judgeLabel() calls. For the file format basics, see output-dev-prompt-file. For error analysis, see output-eval-error-analysis.

Prerequisites

Before writing a judge prompt:

1. Error analysis is complete — You have identified the specific failure mode this judge targets (from output-eval-error-analysis)
2. 20+ labeled examples — At least 20 pass and 20 fail traces for this failure mode, with ground_truth labels in dataset YAML files
3. Code-based check ruled out — Confirmed that Verdict.* helpers (contains, matches, gte, etc.) cannot reliably detect this failure

The Four Components

Every effective judge prompt has exactly four components.

1. Task and Criterion

State the single failure mode being evaluated. Be specific and observable.

Good criteria (specific, observable):

• "Does the blog post maintain a formal tone throughout, or does it slip into casual language?"
• "Does the output contain any URLs that are fabricated rather than drawn from the input?"
• "Does the summary faithfully represent the source material without adding claims not present in the original?"

Bad criteria (vague, holistic):

• "Is this output high quality?"
• "Rate the overall effectiveness of this response"
• "How good is this content?"

2. Pass/Fail Definitions

Define exactly what constitutes pass and fail. Always binary — no Likert scales, no 1-5 ratings, no "partially meets criteria."

PASS: The blog post uses formal language throughout. Professional vocabulary,
complete sentences, no slang, no contractions, no first-person casual asides.

FAIL: The blog post contains one or more instances of casual language: slang,
contractions ("don't", "can't"), informal asides ("pretty cool", "super important"),
or conversational filler ("honestly", "basically").

Why binary: Likert scales create ambiguous boundaries (what's the difference between a 3 and a 4?). Binary forces precise definitions that LLMs can apply consistently and that you can validate against human labels.

3. Few-Shot Examples

Include at least three labeled examples: one clear pass, one clear fail, and one borderline case. Borderline examples are the most valuable — they teach the judge where the decision boundary lies.

Draw examples from your training split only (see output-eval-validate-judge). Never use dev or test examples as few-shot — that's data leakage.

Each example must include:

• The relevant input/output excerpt
• A detailed critique explaining the reasoning
• The verdict (pass or fail)

4. Structured Output

Request JSON output with critique before verdict. This forces the judge to reason before deciding, which improves accuracy.

{
  "critique": "Detailed analysis of the output against the criterion...",
  "verdict": "pass"
}

Always put critique first in the schema. If verdict comes first, the judge commits to a decision before reasoning.

Full .prompt File Example

A judge for the "tone mismatch" failure mode:

# tests/evals/[email protected]
---
provider: anthropic
# current as of 2026-05-04 — run output-dev-model-selection for the latest
model: claude-haiku-4-5-20251001
temperature: 0
maxTokens: 1500
---

<system>
You are an evaluation judge. Your task is to determine whether a blog post maintains the requested tone throughout.

## Criterion

Assess whether the blog post consistently uses the requested tone. A single paragraph that breaks tone is a failure.

## Definitions

PASS: The blog post maintains the requested tone in every paragraph. Word choice, sentence structure, and rhetorical style all align with the requested tone.

FAIL: The blog post contains one or more paragraphs where the tone shifts away from what was requested. Common failures include:
- Formal request but casual language appears ("pretty cool", "super important", contractions)
- Professional request but opinionated editorializing appears
- Technical request but oversimplified explanations appear

## Examples

### Example 1: PASS
Requested tone: formal
Blog excerpt: "The implications of quantum computing for cryptographic security are substantial. Current encryption standards rely on the computational infeasibility of factoring large prime numbers, a guarantee that quantum algorithms may undermine."
Critique: The excerpt uses professional vocabulary ("implications", "computational infeasibility"), complete sentences, no contractions, and maintains an academic register. Consistent formal tone throughout.
Verdict: pass

### Example 2: FAIL
Requested tone: formal
Blog excerpt: "Quantum computing is basically going to break all our encryption. It's pretty wild when you think about it — everything we thought was secure might not be."
Critique: The excerpt contains multiple casual markers: "basically", "pretty wild", contractions ("It's", "might not be"), and conversational filler ("when you think about it"). This directly violates the formal tone request.
Verdict: fail

### Example 3: BORDERLINE (fail)
Requested tone: formal
Blog excerpt: "Quantum computing represents a paradigm shift in computational capability. The technology is incredibly promising, though it's important to note the current limitations in qubit stability and error correction."
Critique: Mostly formal, but contains "incredibly promising" (informal intensifier) and "it's" (contraction). While the overall register is professional, these lapses break the formal tone requirement. Even minor inconsistencies constitute a failure.
Verdict: fail

## Output Format

Return a JSON object with exactly two fields:
- "critique": A detailed analysis (3-5 sentences) citing specific evidence from the blog post
- "verdict": Either "pass" or "fail"
</system>

<user>
Requested tone: {{ requested_tone }}

Blog title: {{ blog_title }}

Blog post:
{{ blog_post }}

Evaluate whether this blog post consistently maintains the requested tone.
</user>

Wiring to judgeVerdict()

After creating the .prompt file, wire it to an evaluator using verify() and judgeVerdict():

// tests/evals/evaluators.ts
import { verify, judgeVerdict } from '@outputai/evals';
import { z } from '@outputai/core';
import { blogInput, blogOutput } from './schemas.js';

export const checkTone = verify(
  {
    name: 'check_tone',
    input: blogInput,
    output: blogOutput
  },
  async ({ input, output, context }) =>
    judgeVerdict({
      prompt: 'judge_tone@v1',
      variables: {
        requested_tone: String(context.ground_truth.expected_tone ?? input.tone ?? 'professional'),
        blog_title: output.title,
        blog_post: output.blog_post
      }
    })
);

Then add it to the eval workflow:

// tests/evals/workflow.ts
import { evalWorkflow } from '@outputai/evals';
import { checkTone } from './evaluators.js';

export default evalWorkflow({
  name: 'blog_generator_eval',
  evals: [
    {
      evaluator: checkTone,
      criticality: 'required',
      interpret: { type: 'verdict' }
    }
  ]
});

Choosing What Context to Pass

Feed the judge only what it needs to evaluate the criterion. Extra context adds noise and cost.

Failure Mode	Required Variables	Not Needed
Tone mismatch	requested_tone, blog_post	topic, input constraints
Off-topic drift	topic, blog_post	tone, length requirements
Hallucinated claims	blog_post, source_material	topic, tone
Faithfulness	summary, original_document	formatting requirements
Missing requirements	requirements_list, blog_post	topic (unless relevant)

Use context.ground_truth for expected values that vary per dataset. Use input.* for values from the workflow input. Use output.* for the workflow output being evaluated.

judgeScore() Variant

Use judgeScore() when you need a numeric quality score rather than binary pass/fail. Apply the same four-component design.

.prompt file for scoring

# tests/evals/[email protected]
---
provider: anthropic
# current as of 2026-05-04 — run output-dev-model-selection for the latest
model: claude-haiku-4-5-20251001
temperature: 0
maxTokens: 1500
---

<system>
You are an evaluation judge. Score the overall writing quality of a blog post on a scale of 0.0 to 1.0.

## Scoring Criteria

- 0.0-0.3: Major issues — incoherent, riddled with errors, or completely off-topic
- 0.4-0.6: Mediocre — readable but has significant quality issues (poor structure, weak arguments, factual gaps)
- 0.7-0.8: Good — well-structured, clear, minor issues only
- 0.9-1.0: Excellent — polished, engaging, publication-ready

## Output Format

Return a JSON object with:
- "critique": Detailed analysis of quality strengths and weaknesses (3-5 sentences)
- "score": A number between 0.0 and 1.0
</system>

<user>
Topic: {{ topic }}

Blog title: {{ blog_title }}

Blog post:
{{ blog_post }}

Score the writing quality of this blog post.
</user>

Wiring to judgeScore()

export const checkQuality = verify(
  { name: 'check_quality', input: blogInput, output: blogOutput },
  async ({ input, output }) =>
    judgeScore({
      prompt: 'judge_quality@v1',
      variables: {
        topic: input.topic,
        blog_title: output.title,
        blog_post: output.blog_post
      }
    })
);

In the eval workflow, use interpret: { type: 'number' } with thresholds:

{
  evaluator: checkQuality,
  criticality: 'required',
  interpret: { type: 'number', pass: 0.7, partial: 0.4 }
}

judgeLabel() Variant

Use judgeLabel() when you need classification into named categories.

export const checkToneLabel = verify(
  { name: 'check_tone_label', input: blogInput, output: blogOutput },
  async ({ output }) =>
    judgeLabel({
      prompt: 'judge_tone_label@v1',
      variables: {
        blog_title: output.title,
        blog_post: output.blog_post
      }
    })
);

In the eval workflow, use interpret: { type: 'string' } with label lists:

{
  evaluator: checkToneLabel,
  criticality: 'informational',
  interpret: { type: 'string', pass: ['professional', 'formal'], partial: ['casual'] }
}

Model Selection

Run output-dev-model-selection to resolve each tier below to a current model ID.

Tier	When to Use	Cost
Smallest in family (speed/cost priority)	Default for most judges. Fast, cheap, good at following structured instructions.	Low
Mid-tier (balance priority)	Complex reasoning required (faithfulness checking, multi-step logical analysis).	Medium
Top-tier (reasoning priority)	Only if mid-tier fails validation. Rarely needed.	High

Always set temperature: 0 for judges. Reproducibility matters more than creativity.

Escalation strategy: start with the smallest tier. If the judge fails validation (TPR/TNR below 80%), move up one tier before rewriting the prompt — the model upgrade alone often fixes it.

Anti-Patterns

• Vague criteria ("Is this good?") — Target one specific, observable failure mode
• Holistic judges ("Rate overall quality on 5 dimensions") — One judge per failure mode
• No few-shot examples — Always include pass, fail, and borderline examples
• Likert scales (1-5 ratings) — Use binary pass/fail for verdict judges; use 0.0-1.0 with defined bands for score judges
• Verdict before critique — Put critique first in the JSON schema to force reasoning
• Skipping validation — Always validate judges against human labels (output-eval-validate-judge)
• Kitchen-sink context — Pass only the variables the judge needs for its criterion
• Few-shot from dev/test set — Only use training-split examples to avoid data leakage

Related Skills

• output-eval-error-analysis — Identify the failure mode this judge targets
• output-dev-eval-testing — Implementation reference for verify(), judgeVerdict(), evalWorkflow()
• output-dev-prompt-file — .prompt file format, Liquid.js templating, provider configuration
• output-eval-validate-judge — Validate this judge against human labels after writing it
• output-eval-dataset-design — Generate diverse datasets for judge validation