Glossary
These terms are English in the source documentation. This page fixes one translation for each and keeps the English original alongside.
- System OneSystem One model
A class of models trained to make fast, structured decisions that software can consume directly. The key difference from generative LLMs is that they produce no text — so there is nothing to parse and nothing to hallucinate. The output is typed values and probability distributions your code can branch, sort and route on.
- state
The material a System One model evaluates: an email, a ticket, a JSON document, a passage of contract text. Every question in a request is evaluated against the same state, independently. A practical consequence: adding questions barely changes latency, and more questions do not degrade the context.
- questionquestion (primitive)
A typed question asked of the state. There are exactly three: choice, score, and noul. They are called primitives by analogy with software primitives — modular, composable, structured. You can mix all three and ask many at once in a single request.
- choice
Pick one option from a defined set. Jev’s Choice accepts up to 255 options. Alongside the selected item you get a probability for each option and an overall confidence. Good for classification, routing, and picking one candidate out of many.
- score
Place the state on an ordered, described scale. The answer is a score, a probability for each level, and confidence. The difference from choice is that the levels are ordered. A common mistake is asking one composite question — decompose into atomic scores and combine them with weights you control in code.
- noul
Ask a yes/no question and get back the probability that the answer is yes (0–1). Not the same thing as confidence: a noul is a probability about the world, confidence is how reliable the model considers its own judgement.
- confidence
How certain the model is about its own judgement. This is the most useful output: the answer tells you what, confidence tells you whether to act on it. The canonical use is gating — auto-apply above a threshold, route to a human below it. Note that not every question type returns it: choice and score do; noul does not, being a probability itself.
- calibration
Whether the probabilities mean what they say: among samples the model calls 70%, about 70% should actually be positive. Both models train for this with reinforcement learning against strictly proper scoring rules (Jev calls it RLCD), and it is the precondition for using confidence as a gate. Uncalibrated confidence is just a number.