LLMs talk. Jev decides. We use LLMs for almost everything, even when the job is simply to make a decision.
• Is this request allowed? Yes / No
• Which route should we take? A / B / C
• What category does this belong to? Category X
• Should this transaction be approved? Yes / No
Do we really need a model that generates a paragraph of text just to answer
that?
TypeSafe AI's new model, Jev, takes a different approach. Instead of generating
text and asking software to interpret it, it returns the decision itself, as a
typed value from a schema you define: a boolean, a choice, a score. Single
forward pass, no parsing, no invalid output.
The numbers make the case: ~70–500ms latency and roughly $0.0004 per decision,
versus multi-second LLM round trips at $0.01–0.15. TypeSafe frames the gap as
two to three orders of magnitude.
The way I see it:
- If a human is going to read the answer: an LLM makes sense.
- If software is going to consume the answer: you probably don't need a model generating text at all.
How many LLM calls in your applications are actually just classification or
decision-making problems in disguise? Curious how others are approaching this in their AI architectures. Swipe for the full breakdown: primitives, cost, hallucination risk, and the
detailed comparison.
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