What is Jev?
Jev is a System One model from TypeSafe AI. Instead of writing long-form text, it turns input into typed Choice, Score, or Noul decisions with probabilities.
Use Jev when software must select from fixed options, score an ordered rubric, or evaluate a boolean proposition. Jev returns structured output and a probability distribution; application code and human policy still control the real action.
Three core outputs
Selects one named option and returns per-option probabilities. Useful for routing, labels, and mutually exclusive classes.
Places input on an ordered rubric. The result is a position on that rubric, not an accuracy percentage.
Estimates the probability that one proposition is true. Useful for a single, clearly bounded check.
When it fits
Good fit
- Ticket or request routing
- Content labeling and moderation suggestions
- Risk, quality, or priority rubrics
- Fast decisions that benefit from probabilities
Poor fit
- Open-ended writing, summaries, or translation
- Treating confidence as guaranteed accuracy
- Unreviewed payment or refund actions
- Tasks without clear label boundaries
Learn and verify
This is an independent community tutorial and is not affiliated with TypeSafe AI. Official material remains the source of truth for the model; thresholds and workflows here are implementation guidance that require calibration on your own labeled data.