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To detect hallucinations in AI-generated content, you can use the metric.uncertainty rule, a semantic entropy based metric that generate different responses for the same input, and compute the entropy of the responses.

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Rule structure:

  • type: metric.uncertainty
  • expected: fail (to flag when the uncertainty is high)
  • threshold: Confidence level for uncertainty detection (e.g., 0.8 for 80% confidence)

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Required input

messages:
- role: system
  content: "You are a helpful assistant."
- role: user
  content: "How is relation of B and D?"
- role: assistant
  content: "D is the same level as B."
We noticed this works better when the system prompt is provided, and the model, and other relevant model parameters is set up on application level.

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Create the policy

Here’s an example of a policy to detect hallucinations: 
{
  "id": "unique policy id",
  "definition": "short description",
  "rules": [
    {
      "type": "metric.uncertainty",
      "expected": "fail",
      "threshold": 0.9
    }
  ],
  "target": "output"
}
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