Glossary
The terms that come up on every page, in plain language.
- State
- The text you send for the model to judge: an email, a ticket, a post, a JSON document.
- Choice
- A question with a fixed list of options. The model returns the most likely option and a probability for every option. Jev allows up to 255 options.
- Score
- A question with ordered levels you write, from 2 to 10 of them. The answer is probability-weighted, so it can fall between levels.
- Noul
- A yes/no question. The answer is one number from 0 to 1: the probability the statement is true.
- Confidence
- How sure the model is of its Choice or Score answer. Your code uses it to decide whether to act or escalate.
- Calibration
- Whether a model's probabilities mean what they say. A calibrated model that says 0.8 is right about 80% of the time.
- ECE
- Expected calibration error. Lower is better. On the published typed-decisions benchmark, Jev scores 0.144 and Laya 0.213.
- Zero-shot
- Answering a new kind of question with no task-specific training.
- Fine-tuning
- Training a model further on your own labeled examples. Possible with Laya's open weights; not possible with Jev.
- Cascade
- A fast model handles every request and passes only the uncertain ones to a slower, more capable model.
- RLCD
- Reinforcement Learning for Calibrated Decisions, TypeSafe's name for the training method behind Jev.
- System One model
- A model that returns typed decisions with probabilities instead of generated text. The name comes from Kahneman's System 1.