Jev vs LLM: When Should You Use Each?
Compare bounded semantic decisions, text generation, explanation, and deterministic computation.
On this page
Match the tool to the outputThe practical distinctionWhen to use JevWhen to use an LLMWhen to use bothWhen to use neitherMatch the tool to the output
| Task | Jev | Generative LLM |
|---|---|---|
| Classification | Yes | Yes |
| Routing | Yes | Yes |
| Bounded decision with probabilities | Native interface | Possible, but different |
| Essay or customer reply | No | Yes |
| Code generation | No | Yes |
| Explanation | No | Yes |
| Open-ended reasoning | Not its intended role | Model-dependent |
| Exact arithmetic | Use code | Use code or a tool |
The practical distinction
Jev receives state and typed questions with a defined answer space. An LLM generates a sequence, possibly constrained by a schema. Both can make classification mistakes. The fact that one output is typed says nothing by itself about whether the decision is correct.
When to use Jev
Use it when your code needs a department, an ordered rubric, or a probability that one proposition is true. Examples include triage, relevance filtering, and deciding which approved handler receives a task. Evaluate probability behavior on your own labeled examples.
When to use an LLM
Use a generative model for prose, explanation, open-ended synthesis, or code. If you need a reply after routing a support ticket, the routing judgment and reply generation can be separate stages. Do not ask Jev to write the message.
When to use both
A cascade can route simple work to code, routine prose to a fast model, and difficult synthesis to a deeper model. Measure total latency, quality, and cost: adding a routing call is not automatically an improvement.
When to use neither
Exact arithmetic, deadline comparison, database lookup, and access control belong in deterministic code. A semantic model may help identify the relevant record, but code must verify ownership and perform the calculation.