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An LLM Judge eval sends the engine’s output to a second model along with a judge prompt. The judge model returns a score (0.0–1.0) and a reasoning explanation. This is the most flexible eval type — you can grade on dimensions like accuracy, tone, completeness, or safety.

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When to use

  • Outputs are subjective or long-form (essays, reports, explanations)
  • Exact match and semantic similarity are too coarse for your quality bar
  • You need nuanced grading with a written explanation per result

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How it works

MIRA sends a structured prompt to the judge model that includes:
  1. Your system prompt (context for the judge)
  2. Your judge prompt with the original input and engine output wrapped in XML tags
  3. Instructions to return JSON: {"score": <0.0–1.0>, "reasoning": "<explanation>", "confidence": <0.0–1.0>}
The XML wrapping is a prompt injection mitigation — the judge is instructed to ignore any directives inside the tagged content.

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Supported judge providers

LLM judge evals support all four providers: AWS Bedrock (default), Anthropic, OpenAI, and Ollama. The judge provider can differ from the primary engine provider.

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Configuring an LLM judge eval

1

Open the Eval Studio

Click the Flask icon in the sidebar, then click + New in the left panel.
2

Select LLM Judge type

Click the LLM Judge type card.
3

Enter a name and system prompt

Give the eval a name. The System Prompt sets the judge’s persona, e.g.:
4

Write the judge prompt

The Judge Prompt describes what to grade. Instruct the judge to return JSON with score (0.0–1.0), reasoning, and confidence. Example:
5

Set the pass threshold

Scores ≥ threshold count as a pass. Default: 0.70 (on a 0–1 scale).
6

Select the judge provider and model

Choose the provider and enter the model ID. A capable model produces more reliable grading. The default is anthropic.claude-3-5-haiku-20241022-v1:0 via Bedrock (cost-effective and fast).
7

Test and activate

Enter a sample input and output in the inline tester, run the test, and click Activate once a test passes.

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Scoring

The judge’s reasoning and confidence are stored for each run and are visible in the run detail view.

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Cost implications

Each LLM judge eval triggers an additional API call to the judge provider. For high-volume eval captures, this can be significant. Consider:
  • Using a smaller/cheaper model for the judge (e.g. claude-3-5-haiku or gpt-4o-mini)
  • Enabling Local-Only Mode in Settings → Evals to suspend LLM judge evals when not needed
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