Overview
Understand Galileo’s Guardrail Metrics in LLM Studio
Galileo has built a menu of Guardrail Metrics to help you evaluate, observe and protect 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
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Correctness (Open Domain Hallucinations)
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Instruction Adherence:
Scorers.instruction_adherence_plus
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Ground Truth Adherence:
Scorers.ground_truth_adherence_plus
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Completeness Luna:
Scorers.completeness_luna
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Completeness Plus:
Scorers.completeness_plus
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RAG Quality Metrics
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Context Adherence (Closed Domain Hallucinations)
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Context Adherence Luna:
Scorers.context_adherence_luna
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Context Adherence Plus:
Scorers.context_adherence_plus
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Chunk Attribution Luna:
Scorers.chunk_attribution_utilization_luna
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Chunk Attribution Plus:
Scorers.chunk_attribution_utilization_plus
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Chunk Utilization Luna:
Scorers.chunk_attribution_utilization_luna
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Chunk Utilization Plus:
Scorers.chunk_attribution_utilization_plus
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Input Quality Metrics
Safety Metrics
Looking for something more specific? You can always add your own custom metric.
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