Jev lacks built‑in probability calibration and requires extra labeled data for calibration
Problem
Users want calibrated probabilities from Jev, but the model does not provide calibrated outputs out‑of‑the‑box; achieving ~80% reliability requires collecting hundreds of labeled examples and applying Platt scaling; additionally, the model’s scores are hard to align with business‑specific rubrics, making confidence guarantees difficult.
Pain points
- Lack of built‑in probability calibration requiring manual labeled data for calibration
- Model scores are difficult to align with business‑specific rubrics, making confidence guarantees hard
- Unreliable confidence predictions for individual examples, limiting practical utility
Questions to verify
- Are you willing to provide labeled data for calibration?
- Can the current model's scores meet your company's specific rubric?
- Do the confidence outputs for individual predictions meet your business requirements?
Evidence
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I tested Jev with a fair die 400 times without telling it the die result. The true probability of face 1 is 1/6, but Jev always chose face 1 and the probability it returned was about 83%. I also tested with a fair coin 200 times and got 0.92 probability. I did several tests and I think Jev is good at problems with a correct answer but weak at problems about actual probabilities whose answers can't be known at all.
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I like this post. I haven't had time to dig into Jev (they aren't accepting new signups), but calibrated probabilities is one of their pitches that caught my attention. And I was wondering how does one offer them on user data. Standard calibration essentially ensures that if a score of 0.8 accompanies a positive prediction (assuming the simple case of binary classification), then if you gathered together all predictions with a score of 0.8, around 80% will be correct. If you have just one example you're sending to a model, how would they guarantee 80% over your data?
quote
Article says that it _can_ be calibrated, it just isn't out of the box. So the title seems contradicted by the body.> If you want calibrated probabilities you’ll still need to recalibrate Jev’s probabilities on your own data. The good news is that is cheap. A few hundred labeled examples from your actual data can be enough to fit a Platt scaling on top of Jev’s scores.
Disclaimer: this card is distilled by AI from community discussions as “pain point evidence”. It is a starting point for need analysis and validation, not a “validated market need”. AI has analytical limits; please open the original discussion above to manually verify before key decisions.