If you are tracking quantities such as latency or cost (e.g. per LLM request), you can use the column average test to assert these quantities are within the expected range.
Some features may have a known average value, and you can use the column average test to assert that it is within the expected range.
If you are writing a tests.json, here are a few valid configurations for the character length test:
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[ { "name": "Average of column 'Age' is greater than 20", "description": "Asserts that the average value of the numeric column 'Age' is greater than 20", "type": "integrity", "subtype": "columnAverage", "thresholds": [ { "insightName": "columnAverage", "insightParameters": [{ "name": "column_name", "value": "Age" }], // Check average on column `Age` "measurement": "columnAverage", "operator": ">", "value": 20.0 } ], "subpopulationFilters": null, "mode": "development", "usesValidationDataset": true, // Apply test to the validation set "usesTrainingDataset": false, "usesMlModel": false, "syncId": "b4dee7dc-4f15-48ca-a282-63e2c04e0689" // Some unique id }]