Getting Started with Jev (TypeSafe System One Model)

1. Is Jev the right tool for your project? Checklist

Good fit

• Task is classification, scoring, filtering or routing
• You need consistent, repeatable outputs for identical inputs
• Output can follow a fixed structured schema

Poor fit

• Need open-ended chat or creative writing
• Requirement for deep multi-step reasoning
• You need natural language paragraph output

Still unsure? Compare roles on the comparison page and skim use cases.

2. How to fit Jev into your existing agent stack

Place Jev at the entry of your agent workflow. Let Jev make routing / classification decisions. Once a decision is made, pass the task to a generative LLM to handle content generation. This separation stabilizes your whole agent pipeline.

Keep confidence thresholds close to the call site. A low-confidence route should escalate instead of pretending certainty. That operational habit matters more than any single model swap. See hybrid agent stack for the short definition.

3. Common pitfalls developers should watch for

1. Do not try to force Jev to generate long text. It is not designed for this.
2. Always define strict input schemas before sending data.
3. Test edge cases to confirm decision consistency.

Add one more check from production experience: freeze a golden set of routing fixtures in CI. If a release changes decision distributions beyond your tolerance, fail the pipeline before users notice silent drift.

4. Next steps to explore Jev SDK

Read our Jev SDK docs for API key channels, pricing, Choice / Score / Noul, and copy-paste HTTP examples. Then review use cases and understand the difference between System One and generative models before building production integrations. The FAQ covers API keys, cost, and local-run questions.