Paste an existing LLM prompt and see which parts are bounded decisions you can move to Jev, and which should stay with the LLM. The analyzer splits your prompt into responsibilities and generates a Decision Spec for each Jev step.
Up to 5,000 characters. Your prompt is not stored — it lives only in your browser session.
Any LLM prompt — system prompt, few-shot, or chain-of-thought.
Each part of your prompt is labeled: Jev, LLM, or code.
Each Jev step gets an editable Decision Spec and code template.
Most LLM prompts do more than one thing. A single prompt might classify input, make a decision, generate text, and check the output — all in one call. Prompt to Jev separates those responsibilities so each one uses the right tool.
The analyzer reads your prompt, identifies each instruction, and labels it: Jev for bounded decisions, LLM for text generation, code for deterministic operations, or Hybrid for mixed steps that need splitting.
Here is how the sample prompt above gets split:
The original prompt did everything in one LLM call. After splitting, three steps move to Jev (deterministic, typed, no retries), one stays with the LLM (text generation), and two become plain code.
After the split, each Jev step gets an editable Decision Spec and code templates in TypeScript, Python, and cURL. You can refine the spec — adjust options, criteria, fallbacks — before generating the final code.
The code templates are deterministic and versioned. Running any implementation requires your own access to Jev from TypeSafe AI. Jev is in Early Access, so the API may change — always validate before production.
No. It does not rewrite or optimize the full prompt. It splits the prompt into responsibilities, labels each one, and lets you migrate the bounded decisions to Jev.
Still have questions?
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