Link	Jev Choice	Jev Score	Jev Noul
Aktualisieren	2026-09-29 04:40:11	2026-09-29 04:40:13	2026-09-29 04:40:15
Webseite	jev-ai.pro [Jev API]	jev-ai.pro/... [Jev API documentation]	jev-ai.pro/... [Jev API documentation]
When to use	Select one label from a fixed, unordered set. Useful for routing tickets to billing, technical, or account queues.	Evaluate an input against an ordered rubric. Useful for urgency levels with explicit definitions for each score.	Estimate whether one clearly stated proposition is true. Useful for a separate escalation flag, independent of the routing label.
Example question	Which team should handle this ticket? Choices: billing, technical, account. Include a fallback policy for unclear inputs.	How urgent is this ticket? Define a rubric such as 1 = routine question, 2 = degraded function, 3 = service blocked. These are illustrative business definitions.	Does this ticket describe an active account-security incident? Phrase one testable proposition rather than combining unrelated conditions.
Result to inspect	choice plus confidence and probabilities. Validate the label against the allowed choices; do not assume confidence equals the winning label probability.	score plus confidence, legend, and probabilities. Interpret the score using the declared rubric, not as a universal measure of severity.	noul: a value from 0 to 1 representing the model's estimated truth probability. It is an estimate, not verified evidence that an incident occurred.
Human review boundary	Route unknown labels, malformed responses, and ambiguous cases to review. Keep refunds, deletions, and access changes behind separate authorization.	Choose action thresholds using a labeled evaluation set and the cost of mistakes. A high urgency score can trigger review without directly executing an action.	Use a review band between positive and negative decisions. Set thresholds with representative cases; do not copy a numeric threshold as a default safety guarantee.
Evaluation approach	Measure per-label precision and recall, plus confusion between queues. Include empty text, mixed intents, unseen topics, and instructions embedded in customer text.	Measure disagreements against human rubric labels, especially large errors. Review whether two annotators interpret each level consistently.	Measure false positives, false negatives, and calibration on labeled statements. Recheck results when input language or the incident definition changes.