Think Fast, Talk Smart: Partitioning Deterministic and Neural Computation for Structured Health Text Generation
For developers of health text generation systems, the paper provides a design rule to reduce errors and costs by partitioning deterministic computation and LLM prompting.
The paper introduces Think Fast, Talk Smart, a pipeline that uses deterministic code for recurring analysis before a single LLM call for structured health text generation. Across 280 user-nights and six models, it achieves lower numeric error, lower instruction-compliance error, and lower end-to-end cost than zero-shot and few-shot baselines.
Large language models (LLMs) are increasingly being used to generate health text from structured records such as wearable time series, biomarkers, vitals, and care-management logs. For recurring health outputs, fluency is not enough: systems must remain faithful to source data, ground explanatory claims in available evidence, follow stated policies, emit machine-readable outputs, and run cheaply enough for repeated use. We ask which responsibilities in structured health generation should be deterministic computation rather than runtime LLM prompting. We introduce Think Fast, Talk Smart, a sleep-health insight pipeline in which deterministic code performs recurring analysis before one bounded LLM writer call. Across 280 user-nights and six models, achieves lower numeric error, lower instruction-compliance error, and lower end-to-end cost than structured zero-shot and few-shot one-call baselines. Layer replacement reveals contract-specific failures: LLM comparison raises numeric error, LLM ranking degrades policy selection, LLM attribution increases unsupported causal language, and an LLM-generated writer interface reintroduces errors even after upstream facts are deterministic. The results support a broader design rule: let code own recurring analysis, and let LLMs express verified facts within bounded interfaces.