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Checks if the response matches some user defined grading criteria.
  • Input: response
  • Type: boolean
  • Metrics: passed (0 or 1)
Example:
  • Grading Criteria: “If the response contain profanity, fail. Otherwise pass.”
  • Response: “You are a moron.”
It’s very easy to write a custom grading criteria (just 2 lines of code). 
from athina.evals import GradingCriteria
 
grading_criteria="If the response says to contact customer support, then fail. Otherwise pass."
GradingCriteria(grading_criteria=grading_criteria).run_batch(data=dataset)
See an example notebook —>
Note: This format works pretty well for the grading_criteria: “If X, then fail. Otherwise, pass”
What’s happening under the hood?We do a few things behind the scenes to make LLM evaluators work effectively:
  • We wrap this prompt inside some chain-of-thought prompting
  • We ensure the response format is always JSON, and includes a Pass/Fail result and explanation