Observability & Evaluation · Software component
Evaluation Trace Sampler
Software componentObservability & EvaluationObservability & Evaluationarc:EvaluationTraceSampler
A monitoring component that selects production reasoning traces for quality evaluation by novelty, low confidence, user flags and systematic random sampling.
Responsibility. Selects production traces for reasoning quality evaluation.
Also known as: Strategic sampling, Sampling-based evaluation, Critical domain sampling
Relationships
is configured by structural
invokes dependency
reads dependency
writes dependency
receives data from dynamic
sends data to dynamic
triggers dynamic
- Human Evaluator Ch3.10
- LLM Judge abstract Ch3.10
Design guidance
- SHOULD evaluate 100% of flagged high-risk outputs (low confidence, anomalies, contradictions, user reports).
- SHOULD raise sampling rates for critical query types (e.g., 100% of medical outputs).
Quantitative guidance
As stated by the sources; verify before use.
- Strategic sampling (10-20% of traffic) plus triggered evaluation gives sufficient drift signal; systematic random sample 5-10% of inferences (Ch3.9).
- Random sampling of ~1% of outputs establishes baseline production rates (Ch3.10).
Classification
- Patterns
- Triggered plus random samplingStratified risk-based sampling
- Quality attributes
- Cost efficiency
- Risks mitigated
- Prohibitive cost of evaluating every output
Sources
- Ch3.9: T. Nguyen, "Reasoning Quality," in Mastering Agentic AI Systems: Guide for the NVIDIA NCP-AAI Exam, 1st ed. 2026, ch. 3.9. ISBN: 9798244538229.
- Ch3.10: T. Nguyen, "Efficiency Metrics," in Mastering Agentic AI Systems: Guide for the NVIDIA NCP-AAI Exam, 1st ed. 2026, ch. 3.10. ISBN: 9798244538229.