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Last updated: 2026-11-02 · Written by the editor

Shipping Jev to Production: A 12-Point Pre-Launch Checklist

Jev's guarantees — valid answers by construction, no hallucinated text, sub-second latency — remove whole classes of failure. They don't remove your failure modes: miscalibrated thresholds, silent provider outages, cost regressions and data-hygiene mistakes all still happen. This is the pre-flight list we'd run before pointing real traffic at a Jev integration.

Correctness (points 1–4)

1. Calibrate on a replay set, not on vibes. Take 100–500 historical cases with known outcomes, run them through your exact states and questions, and pick thresholds from the resulting precision/recall trade-off. Vendor calibration (RLCD) is a property of the model; your operating point is a property of your data.

2. Treat confidence as the API, not the answer. Every automated path should read confidence and have an explicit below-threshold destination (human queue, fallback LLM, retry with more context). "Always trust the top option" is the single most common integration mistake.

3. Version states and questions like prompts. A wording change in a question — or a state-format tweak upstream — is a model change. Hash the question-set and state schema into your logs so any decision can be traced to the exact configuration that produced it.

4. Watch distribution drift, not just errors. Log the histogram of answers and confidences weekly. A slow shift in category mix or a widening confidence spread is often your first signal that input traffic changed — or that a provider swapped something behind the same model id.

Resilience (points 5–8)

5. Put two providers behind one interface. Jev is served by TypeSafe's console, Vercel AI Gateway (typesafe-ai/jev), Cloudflare Workers AI (typesafe/jev) and Vivgrid (jev) at the same reference rate (context differs: 64K, except 32K on Cloudflare). A fallback chain costs minutes to build and turns a single-provider outage into a non-event.

6. Set timeouts and a retry budget. Sub-second latency is typical — which means a 10-second timeout with one retry is generous. Fail toward your below-threshold path, not toward a hanging request.

7. Design the degraded mode before launch. When Jev (and its fallback) are both down, what happens? For triage: everything lands in human review. For gating: fail open or closed, deliberately. Decide in a doc, not at 3 a.m.

8. Respect the rate limits, then confirm them. Early-access defaults are ~250,000 tokens/sec and ~1,200 requests/min. Load-test with your real state lengths — token throughput, not request count, is usually what you hit first with fat states.

Cost (points 9–10)

9. Alert on tokens per decision, not just request count. Your bill is input tokens × volume. A per-decision token metric catches the quiet state-bloat regression (see state design) long before the invoice does. Alert at, say, 1.5x your design-time median.

10. Attribute cost per feature. Tag decisions with the workflow that made them. "Jev costs $40/month" is unactionable; "the spam gate grew 3x this week" is a bug ticket.

Data hygiene (points 11–12)

11. Scrub PII before it becomes a state. Tickets contain emails, phone numbers, order ids. Jev only needs the judgment-relevant text. Scrubbing upstream shrinks cost and shrinks the compliance surface of what you send to any third-party API.

12. Log decisions, not states (or log states deliberately). Full states in your logs are a data-retention liability. Store the question-set version, the answer, the confidence and a state reference — not the raw customer text — unless you've decided the trade-off explicitly.

The 60-second version

Most of this list is ordinary production engineering — which is the point. Jev moves judgment from "unreliable magic" to "measurable component," and measurable components deserve boring, disciplined ops. When you're ready to build: the 50-line triage tutorial is the fastest start, and the cost calculator tells you what your volume really costs.