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Try Japanese support-ticket decisions in the Jev Playground

Souta Aisaka uses Jev’s browser Playground to classify Japanese support requests, change escalation criteria and ask several questions together. The examples distinguish useful judgments from calculations that belong in code.

Original by 相坂ソウタ (Souta Aisaka), GMO天秤AIメディアGetting started

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Our summary

Souta Aisaka’s Japanese walkthrough shows how to try Jev in a browser without building an app. A fictional support ticket describes a blocked invoice download. Separate questions judge urgency, choose the responsible department and rate business impact, returning values rather than a written reply.

The author then keeps another ticket unchanged but describes when it should receive urgent attention. The reported escalation probability moves from 78% to 82%. A later example checks six aspects of an overheating-product complaint together, comparing its displayed timing with a single question about the same message.

These few examples do not establish Japanese-language accuracy or a general speed advantage. One date calculation succeeds, but exact date arithmetic still belongs in ordinary code. Test real wording and decision criteria before adopting the approach. A probability is a model judgment, not proof that a ticket is safe or correctly routed.

Key takeaways

  1. Describe what each answer means in your own workflow. Changing the criteria can change the judgment even when the ticket stays the same.
  2. Ask independent questions about the same message together. The author reports displayed totals of 228 milliseconds for one question and 203 for six; these are example timings, not a benchmark.
  3. Use code for exact calculations and test Japanese text from your own work. A successful date example or confident department choice does not establish general reliability.

Japanese article by 相坂ソウタ (Souta Aisaka) in GMO天秤AIメディア, published and updated 5 October 2026. The browser demonstration includes Noul urgency, Choice department and Score impact questions, with a criteria example moving the reported probability from 78% to 82%. Its one-versus-six-question displayed timing is 228 versus 203 milliseconds. The quoted US$0.29-per-10,000 estimate assumes 700 input tokens per request. Vendor performance claims and product comparisons were not independently verified. This GMO editorial site is unrelated to the existing simota/tenbin experiment.

GMO Tenbin AI Media · Original published

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