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Choose a Jev primitive by the shape of the answer your application needs: Choice for one label from unordered options, Score for a position on defined ordered levels, and Noul for the probability that one yes-or-no statement is true. The distinction matters because each primitive returns a different kind of answer and uncertainty signal.
Choose by the answer your application needs
TypeSafe AI’s official Primitives (Questions) documentation treats Choice, Score, and Noul as distinct typed questions. Think first about what the result should mean downstream:
| Primitive | Answer shape | Good fit | Uncertainty signal |
|---|---|---|---|
| Choice | Selected option, probabilities across the options, and confidence | Classification, routing, or another unordered set | Confidence and the option probability distribution |
| Score | Position on defined ordered levels, with level probabilities and confidence | Severity, ranking, or rubric assessment | Confidence and the level probability distribution |
| Noul | Probability that one statement is true | A single binary proposition | A value near 0.5 signals that yes and no are similarly likely; there is no separate confidence field |
A selected Choice label can select a code branch; a Score can be compared with a threshold; a Noul probability can inform a yes-or-no decision. These outputs are not interchangeable.
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Choice represents categories without an inherent ranking. If the alternatives are “low,” “medium,” and “high,” their order carries meaning, so modeling them as unrelated labels throws that information away. Use Score and define what each ordered level means.
For example, a severity rubric might define levels from minor to critical, with a clear description for each. A Score expresses a position along that rubric; it is not a free-form measurement independent of the levels you supplied.
Mistake 2: Using Score for unrelated labels
Score is for ordered levels, not a way to assign numbers to any set of categories. “Billing,” “account access,” and “shipping” have no natural in-between or ranking. Use Choice when the application needs one category from such a set.
Define the available choices
The official Choice guide says a Choice question accepts up to 255 options. It recommends including an “other” or “none of the above” option when the set may be incomplete. That gives the model a defined way to represent an answer outside the listed categories rather than forcing a misleading fit.
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Mistake 3: Treating Noul as a measure of degree
Noul answers one binary question by returning the probability that its statement is true. TypeSafe AI’s documentation explains: “A Noul value of 0.5 means the model gives yes and no equal probability.” That is balanced uncertainty between true and false—not medium urgency, average quality, or a halfway degree of intensity.
Rank #3
If you need to assess degree, write an ordered rubric and use Score. If you need to know whether one proposition holds, phrase it as a single yes-or-no statement and use Noul. Noul has no separate confidence field; the probability itself is the signal.
Mistake 4: Combining multiple judgments into one question
A question such as “Is the report complete and accurate?” bundles two conditions. A yes-or-no answer cannot show whether the report is complete, accurate, both, or neither. For a clear interpretation, ask one atomic question per proposition.
TypeSafe says questions that share the same state can be evaluated independently in a request and combined in application code. Do not assume one question’s result silently supplies context to another. Keep each question interpretable on its own, then write the combination rule explicitly in your application.
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Before defining a question, write down what your code will do with its result:
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- If it needs a category to route or classify, define a Choice set.
- If it needs a position on a meaningful scale, define ordered Score levels with descriptions.
- If it needs the likelihood of one proposition being true, phrase one Noul statement.
Then check that the answer and uncertainty signal fit the decision. Choice and Score provide confidence and probability distributions over their defined options or levels. Noul provides a probability for truth, with values near 0.5 indicating an uncertain yes/no split. Avoid interpreting any primitive as more precise or more informative than its answer shape supports.
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