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Definition

The JSON score test measures how close the output is to a valid JSON format, evaluating the structural correctness of generated JSON data.

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Taxonomy

  • Task types: LLM.
  • Availability: and .

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Why it matters

  • JSON score is crucial for applications that require structured data output, such as API responses, configuration files, or data extraction tasks.
  • This metric helps ensure that your LLM generates properly formatted JSON that can be parsed and used by downstream systems.
  • It’s particularly important for applications where malformed JSON could cause system failures or data processing errors.

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Required columns

To compute this metric, your dataset must contain the following columns:
  • Outputs: The generated text from your LLM (expected to be JSON format)
This metric evaluates the structural validity of JSON output and doesn’t require ground truth data for comparison.

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Test configuration examples

If you are writing a tests.json, here are a few valid configurations for the JSON score test: 
[
  {
    "name": "Mean JSON score above 0.95",
    "description": "Ensure that the mean JSON score is above 0.95 for valid JSON structure",
    "type": "performance",
    "subtype": "metricThreshold",
    "thresholds": [
      {
        "insightName": "metrics",
        "insightParameters": null,
        "measurement": "meanJsonScore",
        "operator": ">",
        "value": 0.95
      }
    ],
    "subpopulationFilters": null,
    "mode": "development",
    "usesValidationDataset": true,
    "usesTrainingDataset": false,
    "usesMlModel": false,
    "syncId": "b4dee7dc-4f15-48ca-a282-63e2c04e0689"
  }
]