The world of Jev

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.

#AI #AgenticAI #LLM #AIArchitecture #SoftwareArchitecture