Use cases
The official use-case map lists candidate scenarios by industry, all sharing one structure: embed AI decisions into the software workflow while code keeps control. We picked four patterns with clear shape and community traction and wrote them up:
| Scenario | Types used | Pattern in one line | Walkthrough |
|---|---|---|---|
| Model/intent routing | Choice + confidence | Confident → cheap path; unsure → strong model or human | Model routing |
| Content moderation | Score | Rate by risk level; publish/review/remove in code | Content classification |
| Tool approval | Noul + Score | Risk-check every tool call: deny / ask / allow | Tool approval |
| Ticket triage | All three | One request: classify, gauge urgency, detect refunds | Ticket triage |
Why these four
Section titled “Why these four”They share the profile of work Jev is good at (our framing):
- High frequency: thousands of judgments a day, sensitive to latency and unit price
- Narrow judgment: one question, a closed answer set (options/levels/yes-no)
- Probabilities over prose: the consumer is a code branch, not a human reader
- Reviewable: confidence and logs retained; edge cases can go to humans
Official patterns worth knowing
Section titled “Official patterns worth knowing”From the official Patterns docs (via the llms.txt index):
- Speculative fan-out: send a batch of “maybe relevant” questions in one request and let code pick answers — no round trips for branchy judgment trees
- Composite scoring: split a complex judgment into atomic Scores; merge with your own weights in code
- Intent routing: classify first, then dispatch to deterministic logic / a specialist model / a human
The community has open-source implementations (router plugins, MCP servers, safety gates) — see Ecosystem. For measured claims (LangChain’s 500-eval study, Vercel numbers relayed by TechCrunch), check the verification table in Limitations before quoting any figure.
Three questions before adopting
Section titled “Three questions before adopting”- Can this judgment’s answers be enumerated as options/levels/yes-no? If not, Jev doesn’t fit
- What does a wrong judgment cost, and is there a human-review fallback? If not, be careful with automation
- Is your current approach pure rules or a big model doing everything? The former may not need a model; the latter may only need Jev up front
For code you can run today, start with Ticket triage — it ships with site examples (JS / Python).