Jev AI Tools FAQ
Independent answers for builders evaluating Jev AI tools patterns. Not official TypeSafe support.
Jev AI FundamentalsWhat is Jev AI and what makes it a "System One" model?
Jev AI is TypeSafe AI’s model specialized for high-speed, structured decisions rather than text generation. Drawing on Kahneman’s System 1 / System 2 framing, generative LLMs behave more like slow deliberative writers, while Jev returns Choice, Score, and Noul primitives. This directory explains how to compose those primitives into Jev AI tools workflows — it is not the official TypeSafe product site.
Economics & PerformanceWhy are output tokens described as free for Jev?
Official TypeSafe materials price input at $0.042 per 1M tokens and state that output tokens are free because answers are fixed-dimension primitives rather than long token streams (source: typesafe.ai, 2026-09). This site does not sell API access and does not independently meter your bill.
Reliability & TypesHow do typed answers reduce schema failures?
Per TypeSafe docs, Choice and Score answers are constrained to the criteria you supply; the model returns distributions over those options/levels rather than free-form JSON strings. Your application still must validate HTTP errors and threshold confidence/noul values appropriately.
RLCD MethodologyWhat is RLCD (Reinforcement Learning for Calibrated Decisions)?
TypeSafe describes RLCD as training that aligns reported probabilities with empirical accuracy so thresholds are usable in automation. Treat vendor calibration claims as something to verify on your own labeled set before production cutover.
Ecosystem & UsageCan Jev AI replace general LLMs for writing or code creation?
No. Jev is designed for snap judgments, not prose or code generation. Use Jev AI tools patterns for routing, guards, and classification; keep System Two models for writing and synthesis. This directory is a third-party resource and is not affiliated with TypeSafe AI.