When people hear AI, they often picture a chat window that returns a written answer, or a tool that creates an image or drafts an article. Much of the software around us needs something shorter. It may need to know whether a document is an invoice or meeting note, which queue should receive a request, or whether a person should review a message.
Jev is a TypeSafe AI model built for this kind of evaluation. You send source text with questions whose answer shapes are already defined. Jev returns structured results that software can use. Its main job is making judgments against stated options and criteria, while conversational and generative AI generally focuses on producing text for a person to read.
Many general language models can also return structured results. Jev is another option designed specifically for evaluation, so a team should compare it with the tools it already uses on the same task.
Picture a shared folder full of documents
Suppose a team receives text files from several departments. Each file belongs in one of four categories: invoice, expense claim, meeting note or other. The team does not need AI to rewrite the documents. It wants a suggested category after the model reads each one.
Jev receives the text and those four options. It returns the option with the greatest weight, a probability distribution across every option and confidence that reflects whether the weight is concentrated on one answer or divided among several. The Choice documentation defines these three parts of the result.
A document that starts with an invoice number and lists purchased items may place nearly all its weight on invoice. A file that contains a supplier quote and a request to make a payment could split the weight between categories. That ambiguity is useful. The workflow can send the file to a person instead of forcing a category and continuing as though the answer were settled.
An other option also matters. If the list does not cover the material people actually submit, the model must otherwise pick the nearest answer even when none fits. The Choice guidance recommends other or none of the above when the option set may be incomplete.
Choice, Score and Noul answer different questions
TypeSafe defines three Jev question types. Its introduction recommends keeping each question narrow. When a decision depends on several factors, ask about them separately and combine the results in software.
Choice applies when the answer is one item from a defined set, such as a document category or the team that should receive a request. It returns the selected option, probabilities for every option and confidence.
Score applies when the answer lies on ordered, described levels. A team might rate document completeness from "insufficient information" to "all required details present." The descriptions must separate the levels clearly. A Score can sit between levels because it is calculated from the weight across all levels, rather than selected as an arbitrary fixed rating.
Noul evaluates a yes-or-no statement, such as "This document states a payment due date." Its result is a value between 0 and 1. Noul has no separate confidence field, so a rule written for Choice or Score confidence should not be copied over to it.
All three question types can use the same source text, but Jev evaluates each question independently. Asking which category a document belongs to should not change the answer to a separate question about whether it includes a due date.
