Jev for data teams
Data teams should treat Jev as an external labeling/feature function with a model id column, not as a source of numeric truth.
Unofficial. AutoResearch cookbook: Jev features into a classical model. docs.typesafe.ai.
Warehouse hygiene
Store model, question git SHA, full probability maps, and the action taken. Mark tables as non-deterministic hops. Do not join as if Nouls were identities.
What not to ask Jev
Aggregates, unique counts, date diffs, and “expected revenue from a Score.” Those are SQL.
Feature store pattern
Write Jev outputs as a narrow table: entity_id, as_of, model, question_sha, noul_or_choice, probabilities (JSON), action. Downstream models can consume them (AutoResearch-shaped). Do not overwrite source-of-truth facts with a Noul.
Calibration is your job. TypeSafe trains for calibrated decisions — that is a vendor claim. Plot reliability on your labels before you use a Noul as P(churn).
What this page does not claim
- No ECE figures invented.
FAQ
Can Jev replace our labeling vendor? Only for snap judgments you can define. Still sample humans.
Streaming features? Call from workers with RPM caps. See vs streaming.
Disclaimer
This is an independent unofficial site and is not affiliated with TypeSafe AI; official documentation is available at https://docs.typesafe.ai. Never treat jev.pro as TypeSafe official documentation. We do not sell, issue, or proxy API keys.
Open-cluster pages are independent field-guide notes. Replicas and third-party interfaces mentioned anywhere on jev.pro are not Jev and not endorsed. Hub: Open. Siblings: evaluation guide, offline evaluation, vs deterministic pipelines. Canonical: https://docs.typesafe.ai.
Sources
Public TypeSafe or adjacent documentation only. No private claims.