Structured information extraction at small-language-model scale is a routine production workload, yet each new task added to a serving stack can demand new task-specific model state. We propose a schema-hierarchical soft prompt: a small learned prefix, trained on a frozen backbone, that gives every JSON-schema node its own slot, orders the slots by the schema, and marks tree depth with learned embeddings. Used alone on the open USPTO-ORD patent task, it recovers most of a LoRA reference’s extraction quality while training more than an order of magnitude fewer parameters, and ablations attribute the gain to depth and schema order rather than token count. Used with a LoRA adapter on an internal booking task, the same slots stabilize one of two held-out categories, so we report this as a bounded mechanism study rather than a general gain. Inference-time slot replacement indicates that position supplies the schema scaffold while learned content stabilizes generation. Measured serving gives a far smaller task artifact and higher throughput than multi-LoRA as interleaved tasks grow. The soft-prompt module is available at https://github.com/microsoft/Hierarchical-Soft-Prompt.