Aligning Jev and other decision models to your data

The questions people arrive with

Can you fine-tune Jev?

No. TypeSafe serves the same Jev weights to every account, so there is no fine-tuning endpoint. You get the same effect by changing the questions you ask it and fitting a small decision head over its answers, aligned from reviewer feedback. On 140 labels, one new plain-English question raised accuracy from 77 to 87 percent with the model unchanged. Fine-Tuning Jev: You Can't. Here's What Gets You the Same Effect.

How do I align Jev to my own data?

Start classifying immediately, review the decisions as it makes them, and label the ones a reviewer disagrees with. Those labels refit the decision head and calibrate its confidence, and when disagreements keep coming, they tell you which question to add. That loop is far cheaper than building a labelled dataset before you start. Plexus Is a Classifier Lab.

Can you trust Jev's confidence?

Higher really does mean more likely right, but out of the box it ran about 15 points overconfident on 8,801 labelled examples. Calibration against a few hundred of your own labels closes most of that gap, and only then can you set a threshold to auto-accept above and route the rest to a person. Can You Trust Jev's Confidence?.

Does a decision model change its verdict on a name or a pronoun?

It can. On two thousand real professional bios per job with one pronoun swapped, Jev's verdict moved on 1 to 4 bios in 100 and the open-weights Laya's on 8 to 18, almost always toward the stereotype. Auditing by slice before and after deployment is how you catch it. The One-Word Test.

Can I own the classifier instead of paying per request forever?

Yes. Once the hosted model is aligned, you can distill it into a small model you serve yourself. A 66-million-parameter student trained from an aligned Jev system scored 91.2 percent against the human label, above its teacher's 89.0, at 5 to 15 milliseconds an item, with a per-slice ship gate deciding when it was ready. Distilling an Aligned Jev System into a Classifier You Own.

Should I use Jev or an open-weights decision model like Laya?

The same reviewer loop aligns either one. On the same 140 labels and the same questions, Jev led Laya by 4.7 points alone and 6.8 with the feedback layer, and fully fine-tuning Laya on those labels beat both. Which engine you pick depends on whether you need to own the weights. Jev vs Laya.

The loop, in order

1. Start classifying now. Put the decision model in front of real cases with the questions your reviewers already ask.
2. Review and label the disagreements. Reviewers agree or disagree with verdicts as they happen. That is the whole labelling effort.
3. Calibrate the confidence. Fit the stated confidence to observed accuracy so a threshold means what it says.
4. Change the questions when refits stall. Let the system read the disagreements and propose one question; a person approves it.
5. Gate every change by slice. Promote a version only when it clears held-out labels on every slice reviewers care about.
6. Distill when you're ready to own it. Train a small model from the aligned system and ship it behind the same gate.

Every step above is a published experiment on the research page, and Plexus is where we run the loop in production.