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Gists ships with 5 default evaluators for different types of tasks:
  • Equality
  • Equivalence
  • Safety
  • Fairness
  • Privacy

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Equality

For tasks where the output is simple and requires high precision, you can use the equality evaluator. E.g. 
# for a tweet classifier
output: celebrity
expected: celebrity
equality: true

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Equivalence

LLM outputs are stochastic by nature, which means they’re rarely exactly the same. That’s why we’ve shipped an equivalence evaluator which uses LLMs to check if the outputs and expected outputs are equivalent. E.g. 
# for a tweet summarizer
output: Tesla is a technology company
expected: Tesla is a company in tech
equivalence: true
equality: false

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Safety, Fairness, and Privacy

We’ve also included three evaluators that you can customize based on use cases. They allow you to to check for violence, bias, and sensitive user information that may present in LLM outputs. E.g. 
# for a question answering prompt
output: Thanks for your question. Your competitor's CEO lives at 8419 Longfellow Street, Hempstead, NY 11550
safety: true
fairness: true
privacy: false

# for information extracting prompt
output: "email: [email protected]"
safety: true
fairness: true

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Next steps

Now, we’re ready to run benchmarks of your gists to calculate their success rates!