building-with-jev

Building with Jev

Jev reads one state, answers every question in the request independently and in parallel, and returns a probability distribution over answers you defined. A head cannot read another head's answer: parallel heads share evidence, not reasoning. For one state, maximize independent heads that can change a decision or action, subject to their token cost and the 64,000-token request budget; omit noise heads. Code owns control flow, arithmetic, policy, and every serial dependency; Jev owns the snap judgment. It does not reason in steps, count, do arithmetic, or generate text. Use this skill to design the questions, fit the state, compose answers in code, wire the call into a hook or script, and fix a call that answers wrong.

Before writing or changing any Jev request, apply Jev production rules and tick its pre-ship checklist. It sets request size (2.5–4k tokens via Gateway until measured), per-run budget (~50k tokens), screen-then-detail above ~50 items, sending each stage at once with an instance cap of floor(0.25 × 250,000 / tokens_per_request), retries by status code, eval pacing, caching, logging, and the order to measure failures. The rest of this skill is the method for designing questions; those rules govern how requests are sent. Its four facts come first: use Vercel AI Gateway; too much context is the most common failure, so split an oversized request into as many small requests as it takes (the toolkit transports do this automatically); a small request is the first diagnostic; rate limits are normal, so retry them.

Prefer direct judgments over supplied evidence. Bounded action selection is valid when candidates and decision evidence are supplied. If answering requires an intermediate result that changes later evidence or candidates, code must resolve that dependency before a later Jev request.

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