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A Voyage AI practitioner argues that embedding inputs with a cheap embedding model and training a simple sklearn classifier on labeled data beats hype-driven zero-shot decision models like Jev, reporting a 37% error reduction at lower cost (with the caveat that this advantage only applies when training labels are available).