Using Jev to route messages and reduce LLM calls

Jev, a TypeSafe AI decision model, evaluates supplied text or structured state and returns typed classifications, scores, and probability estimates that applications can use for routing rather than generating customer-facing content. Its Noul or boolean questions estimate yes-or-no likelihoods, Choice questions select among defined categories, and Score questions assess ordered levels such as customer frustration, enabling uses such as support triage, moderation, and deciding whether an LLM or account-data lookup is needed. In a WhatsApp chatbot experiment, Jev was placed before a generative LLM to identify messages that could receive predefined replies, require conversational context, or need more complex handling, potentially reducing unnecessary LLM calls. The integration requires developers to define categories, thresholds, fallback behavior, and error handling, while validating results against labeled examples rather than treating confidence or probabilities as guarantees of correctness. Jev is less suitable for calculations, date handling, counting, or multistep reasoning, which should remain in ordinary application logic, and its practical value depends on measured improvements in routing accuracy, cost, latency, and customer outcomes across the complete workflow.

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