Precision and Recall
HoldTechniques
Evaluation metrics used to measure false positives and missed detections.
Why it's here
Placed in Hold: 1 article(s) of evidence from 1 source(s), led by research-stage coverage, with 0 in the last 30 days. Confidence 24%. Low accumulated evidence, so it defaults conservatively pending more signal.
Evidence (1)
- 5GitHub Blog·8/25/2026researchHow to evaluate LLMs before production
GitHub outlines a production-focused approach to evaluating LLM systems, emphasizing that benchmark gains may not reflect real-world performance. The post recommends starting from the product decision, then using primary outcomes, safety constraints, and operational guardrails to judge tradeoffs such as false-positive reduction, recall, latency, cost, and reliability.