3 ms·
in short: it's faster, cheaper, smart structured output. each "question" is answered in parallel instead of a sequential (like an LLM). so if you have an input
by mritchie712 14d ago
in short: it's faster, cheaper, smart structured output.
each "question" is answered in parallel instead of a sequential (like an LLM). so if you have an input like:
{"is_it_hotdog": noul, "is_it_apple", noul}
it answers is_it_hotdog and is_it_apple in parallel and gives a probability.
- satvikpendem 14d agoCan't I just parallelize my LLM calls myself for each question?
- orbital-decay 14d agoYou can. It will be expensive, slow, and less reliable than a specialized model.
- zwily 14d agoAnything you can do in Jev can be done with an LLM at much greater cost and latency.
- mmnfrdmcx 14d agoAgree, except the probabilities for outcomes in the structured output. I don't think you can get those for most frontier LLMs (logprobas). You can get it for open source models but not frontier LLMs.
- esafak 14d agoThat number is a big deal, assuming it is well calibrated. Did they talk about calibration?
- Matticus_Rex 14d agoI've seen them talk about it a bit on Twitter -- it seems to be fairly well-calibrated in general, but obviously you need to test it on your use case and dial it in comparison with known data for best results.
- shados 13d agoYup. LLMs can do almost anything. Can they do it at the speed, cost and confidence of a model like Jev is a different story. In Jev output tokens are straight up free because it's not doing text token generations.