Writing
Verified, Not Trusted: Expense Analysis, Part Four
Evals measure reliability with reference keys, code graders, model judges and pass-rate gates. Expense examples show what to check and how structured output expands coverage.
Topic
Jev
TypeSafe AI’s System One model for typed judgments about supplied text. Choice, Score, and Noul support bounded semantic decisions.
4 pages
Writing
Verified, Not Trusted: Expense Analysis, Part Two
Imperative Python controls expense analysis and verifies model contributions through source evidence, reconciliation, and exact calculations. Models handle bounded judgments.
Writing
Jev: A Practical Reference
Jev makes bounded semantic judgments with Choice, Score, and Noul; code handles exact work and policy, while evaluation and confidence gates determine when to act or escalate.
Writing
Using Jev Compact LLM Context
Jev can shorten LLM context by scoring which tool events a current question still needs. A recorded Noul response and Python retention code show what stays and why the threshold matters.