Jev documentation: choice, score and Bernoulli question types
TypeSafe's Jev is a structured decision model designed to replace LLMs in software pipelines where code must consume AI output directly. It accepts typed questions alongside a state object and returns typed values — choices, scores, or B…
- 01The model evaluates all questions in parallel and in isolation against the same state, meaning additional questions add minimal latency and no context degradation.
- 02TypeSafe recommends decomposing complex judgments into single-factor atomic questions, then combining results programmatically, keeping weighting logic in code rather than prompts.
TypeSafe's Jev is a structured decision model designed to replace LLMs in software pipelines where code must consume AI output directly. It accepts typed questions alongside a state object and returns typed values — choices, scores, or Bernoulli-style true/false outputs — with probability distributions and confidence levels, eliminating the need for text parsing. The model evaluates all questions in parallel and in isolation against the same state, meaning additional questions add minimal latency and no context degradation.
Read the full article at docs.typesafe.aiTypeSafe's Jev is a structured decision model designed to replace LLMs in software pipelines where code must consume AI output directly. It accepts typed questions alongside a state object and returns typed values — choices, scores, or Bernoulli-style true/false outputs — with probability distributions and confidence levels, eliminating the need for text parsing. The model evaluates all questions in parallel and in isolation against the same state, meaning additional questions add minimal latency and no context degradation. TypeSafe recommends decomposing complex judgments into single-factor atomic questions, then combining results programmatically, keeping weighting logic in code rather than prompts.
Don't miss tomorrow's
The Daily Pulse in your inbox each morning — sourced and linked.