2 posts tagged “jev”
2026
I built this new plugin for LLM to add support for TypeSafe AI's new Jev model. Install it like this:
llm install llm-typesafe
Then set an API key (get one here, the waitlist seems to move pretty fast):
llm keys set typesafe
# Paste key
And now you can ask yes/no "noul" questions like this:
llm -m jev 'Please refund my last payment.' \
-s 'Does this message explicitly request a refund?'
Output:
{"type": "noul", "noul": 0.99}
Or choice questions like this:
cat message.txt | llm -m jev \
-s 'Which team should handle this message? If billing and technical issues both occur, choose billing.' \
-o answer_type choice \
-o criteria '{
"billing":"Charges, invoices, payments, or refunds",
"technical":"Problems installing or using the product",
"other":"Neither category fits"
}'Or scoring questions like this:
cat report.txt | llm -m jev \
-s 'How reproducible is the problem described in this report?' \
-o answer_type score \
-o criteria '[
"No reproduction instructions",
"Some instructions, but important steps are missing",
"Complete steps with expected and actual results"
]'See the README for more details.
Jev introduces a new shape of LLM—System One, aka Decision Models
Last week TypeSafe AI unveiled Jev, their first example of a new category of model that they are calling “System One models” (I’m with Maggie Appleton, I think “decision models” is a better name for these). Jev is an interesting variant on the usual LLM format: it still accepts text inputs, but instead of text output it returns floating point numbers corresponding to categories, yes/no questions, ratings, and associated confidence scores.
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