Choose Jev when the answer space is bounded
If your application already knows the actions it can take, a typed decision can be a better interface than free-form prose. Examples include selecting a support queue, assigning a risk band or deciding whether a workflow needs human review.
Choose a generative LLM when you need new strings
Use a generative model when the value of the task comes from producing language, code or a flexible multi-step response. Jev intentionally does not replace those capabilities.
Use both when the workflow needs both
A useful architecture can pair them: a generative model creates or transforms content, while a decision model classifies, routes, verifies or gates a later step. The surrounding code remains responsible for policy and enforcement.
What “zero hallucinations” does—and does not—mean
TypeSafe AI uses this language to describe Jev's inability to invent arbitrary output strings outside the declared type. It should not be interpreted as “every judgment is factually correct.” A decision can be well-typed and still be wrong. Evaluation and uncertainty handling remain essential.
Estimate the economics
Use adjustable inputs rather than relying on a headline multiplier from one benchmark.
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