Six tasks from the SMM4H–HeaRD 2026 workshop were addressed with task-specific large-language-model (LLM) pipelines relying on prompt engineering, strict structured (JSON) responses, and deterministic rule sets. The pipelines utilize no task-specific fine-tuning and can be adapted across diverse clinical and social media data. This study demonstrates that general-purpose LLMs (gpt-5.4-mini and gpt-5.4) can accurately extract and classify crucial health information when constrained by strict output schemas. Notably, our hybrid approachachieved the best overall performance among all participating systems for Task 2 (Insomnia Detection).