Jev vs LLM vs classifier: which should you use?
Compare TypeSafe Jev with generative LLMs, deterministic rules, and task-specific classifiers by output shape, task fit, uncertainty, latency, and operational control.
1. Start with the output contract
Jev returns a typed Choice, Score, or Noul result over options you declare. A generative LLM returns tokens. A classifier returns labels from a fixed training target. Ordinary code returns an exact result from rules. Choose the interface your application actually needs.
| Tool | Best fit | Output | Main risk |
|---|---|---|---|
| Jev | Bounded semantic judgment | Choice / Score / Noul | Typed but still possibly wrong |
| Generative LLM | Generation and extended reasoning | Text or structured output | Output drift and parsing failure |
| Deterministic code | Exact rules and facts | Exact result | Rules miss semantic ambiguity |
| Dedicated classifier | Stable, high-volume label task | Fixed labels and probabilities | Training data and model operations |
2. Use Jev for bounded semantic judgment
Routing, ranking, labeling, relevance, and risk bands are strong candidates when the right answer depends on meaning rather than a hard lookup. Your code should still validate the response, set thresholds, and own every side effect.
3. Keep generation and long reasoning with an LLM
Writing, summarization, translation, coding, planning, and open-ended dialogue need outputs Jev is intentionally not designed to produce. A useful hybrid pattern lets Jev select a path or gate a request, then lets a generative model do the work that creates new text.
4. Prefer rules or a classifier when they are enough
Permissions, arithmetic, schema checks, allowlists, and exact policy belong in code. A dedicated classifier can be attractive for a mature high-volume task with representative labels, a stable taxonomy, and a team able to train, deploy, and monitor it.
5. Evaluate the complete system
Do not compare headline speed or price alone. Measure decision quality on your own labeled cases, end-to-end latency, provider failures, review rate, cost of mistakes, calibration by risk band, and how quickly the team can update the decision contract.