Explainers
What Jev is, what System One means, and how the model is meant to be used.
Atomic questions, composed in code
Ask one snap judgment per question. Split multi-factor work. Combine answers with weights you own.
Confidence scores and escalation in Jev | TypeSafe Jev System One
Learn when a decision should escalate — Jev confidence escalation. Independent unofficial notes; confirm against docs.typesafe.ai.
Context selection for Jev decisions | TypeSafe Jev System One
Understand which context a decision needs — Jev context selection. Independent unofficial notes; confirm against docs.typesafe.ai.
Decision ownership and accountability in Jev | TypeSafe Jev System One
Clarify ownership for automated decisions — Jev decision ownership. Independent unofficial notes; confirm against docs.typesafe.ai.
Decision traces and auditability in Jev | TypeSafe Jev System One
Learn how to make decisions auditable — Jev decision traces. Independent unofficial notes; confirm against docs.typesafe.ai.
Deterministic outputs for Jev decisions | TypeSafe Jev System One
Understand repeatable decision results — deterministic Jev outputs. Independent unofficial notes; confirm against docs.typesafe.ai.
Evidence-first decision design for Jev | TypeSafe Jev System One
Learn how evidence supports decisions — evidence-first Jev. Independent unofficial notes; confirm against docs.typesafe.ai.
Human-in-the-loop Jev workflows | TypeSafe Jev System One
Understand human review handoffs — Jev human in the loop. Independent unofficial notes; confirm against docs.typesafe.ai.
Jev 1.13: the current public model
jev-1.13.0 is the versioned ID behind jev-latest and jev-preview as of the public models page. Pin it if you tune thresholds.
Jev data handling and customization
TypeSafe says Jev is not trained on customer requests. It is not LoRA-adapted per account. Enterprise ZDR is a legal topic, not a marketing claim we invent.
Jev decision boundaries
How Jev’s closed label set, confidence, and code thresholds draw the decision boundary — and where a trained classifier still wins.
Jev decision latency
What TypeSafe’s 70ms–500ms and 40x–200x (and 193.6x) claims measure — and how to time your own path. No invented numbers.
Jev language support
English is Jev's primary training language. Other languages are accepted with currently lower accuracy.
Jev offline evaluation
Run your own Jev eval harness: labeled tickets, frozen questions, pinned jev-1.13.0. Do not invent 193x numbers.
Jev System One versus System Two reasoning | TypeSafe Jev System One
Compare fast decisions with deliberation — System One and System Two decisions. Independent unofficial notes; confirm against docs.typesafe.ai.
Jev vs an LLM: the official contrast
TypeSafe trains Jev for calibrated decisions, not generated text. Here is the contrast they publish — and the caveats they attach.
Measuring decision quality with Jev | TypeSafe Jev System One
Learn how to assess decision quality — Jev decision quality. Independent unofficial notes; confirm against docs.typesafe.ai.
Model-agnostic Jev decision design | TypeSafe Jev System One
Understand portability across models — model-agnostic Jev. Independent unofficial notes; confirm against docs.typesafe.ai.
Policy-aware decisions with Jev | TypeSafe Jev System One
Understand policy checks in decisions — policy-aware Jev decisions. Independent unofficial notes; confirm against docs.typesafe.ai.
Production feedback loops for Jev | TypeSafe Jev System One
Improve decisions with observed outcomes — Jev production feedback. Independent unofficial notes; confirm against docs.typesafe.ai.
RLCD: Reinforcement Learning for Calibrated Decisions
TypeSafe trains Jev with RLCD — reinforcement learning aimed at epistemically honest probabilities on System One tasks.
Safe defaults in TypeSafe Jev | TypeSafe Jev System One
Design conservative fallback actions — Jev safe defaults. Independent unofficial notes; confirm against docs.typesafe.ai.
Signals and actions in a Jev decision | TypeSafe Jev System One
Understand how signals become actions — Jev signals and actions. Independent unofficial notes; confirm against docs.typesafe.ai.
State and memory across Jev decisions | TypeSafe Jev System One
Understand stateful decision workflows — Jev state and memory. Independent unofficial notes; confirm against docs.typesafe.ai.
State: the material Jev evaluates
State is a string, object, or array of text. All questions in a request see the same state. Images and audio are not supported.
TypeSafe Jev System One decision model overview | TypeSafe Jev System One
Understand how the model works — TypeSafe Jev decision model. Independent unofficial notes; confirm against docs.typesafe.ai.
Uncertainty handling in the Jev model | TypeSafe Jev System One
Learn how to represent uncertainty — Jev uncertainty handling. Independent unofficial notes; confirm against docs.typesafe.ai.
What is a System One model?
System One models make fast, structured decisions for software. Jev is TypeSafe's flagship and first model in that class.
What is Jev?
Jev is TypeSafe AI's flagship System One model: unstructured state in, typed probabilistic decisions out. No text generation.
Why Jev gives up string generation
TypeSafe argues that dropping free-text output buys type safety, parallel answers, and software-shaped use.