Galileo Guardrail Metrics
Understand Galileo's Guardrail Metrics in LLM Studio
Galileo has built a menu of Guardrail Metrics to help you evaluate and observe your generative AI applications. These metrics are tailored to your use case and are designed to help you ensure your application quality and behavior. The Scorer
definition for each metric is listed immediately below.
Galileo's Guardrail Metrics are a combination of industry-standard metrics and an outcome of Galileo's in-house ML Research Team.
Output Quality Metrics
Correctness (Open Domain Hallucinations)
Completeness Luna:
Scorers.completeness_luna
Completeness Plus:
Scorers.completeness_plus
RAG Quality Metrics
Context Adherence (Closed Domain Hallucinations)
Context Adherence Luna:
Scorers.context_adherence_luna
Context Adherence Plus:
Scorers.context_adherence_plus
Chunk Attribution Luna:
Scorers.chunk_attribution_utilization_luna
Chunk Attribution Plus:
Scorers.chunk_attribution_utilization_plus
Chunk Utilization Luna:
Scorers.chunk_attribution_utilization_luna
Chunk Utilization Plus:
Scorers.chunk_attribution_utilization_plus
Input Quality Metrics
Safety Metrics
Looking for something more specific? You can always add your own custom metric.
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