Applying large language models to Traditional Chinese Medicine (TCM) prescription generation reveals three clinically critical gaps:
- models produce end-to-end mappings without auditable reasoning following the li-fa-fang-yao paradigm (SR Gap),
- treat each encounter in isolation without follow-up adjustment via sui zheng jia jian (LA Gap),
- and fail to enforce absolute contraindication rules such as Shi Ba Fan (SC Gap).
We propose a progressive four-stage framework (SFT $\to$ PG-CoT $\to$ Dynamic $\to$ K-RL) that addresses each gap:
- PG-CoT constrains CoT distillation under the li-fa-fang-yao paradigm to produce auditable diagnostic chains,
- Dynamic SFT models patient trajectories with explicit transition reasoning,
- and K-RL encodes deterministic pharmacological rules as rule-based DPO preference signals.
Across 12 fine-tuned models and 6 zero-shot baselines, our framework substantially improves prescription quality over zero-shot baselines---with a 7B model (Mistral-7B) surpassing zero-shot GPT-5 on all three TCM evaluation metrics.