STEAD-PE: A Structured Static-Dynamic PE Framework for Arabic Medical Response Generation

Medical response generation in low-resource and linguistically complex languages such as Arabic requires precise, context-aware advice, making Prompt Engineering a critical mechanism for controlling large language model behavior and improving output quality. The demand for scalable and reliable Arabic medical support systems has increased interest in prompt-based controllability, particularly where model fine-tuning and large annotated datasets are impractical. However, existing LLMs often show limited clinical relevance and semantic alignment when prompted with unstructured, patient-authored Arabic medical queries. In this work, we propose ST ructur E d st A tic- D ynamic PE (STEAD-PE), a prompt-only framework for Arabic medical response generation. STEAD-PE operates without model fine-tuning or parameter updates and leverages expert-curated Arabic medical posts paired with clinically validated treatment plans. The framework introduces case-specific structured metadata Domain, Severity, and Diagnosis—dynamically injected at inference time to enable clinically grounded and context-aware responses. We evaluate STEAD-PE across thirteen state-of-the-art LLMs, including Arabic-specialized, multilingual, and general-purpose models, using BERTScore for semantic similarity. Results show consistent and monotonic improvements in semantic and clinical relevance across all models, with STEAD-PE (Dynamic) achieving the highest BERTScore-measured semantic alignment in every case. Among evaluated models, LFM2-350M attains the best overall BERTScore (86.50%), while Qwen-32B improves from 80.89% (Zero-Shot) to 84.90% (Static) and 85.76% (Dynamic). These findings establish STEAD-PE as a robust, model-agnostic PE framework and highlight the importance of dynamically injected clinical context in reducing linguistic ambiguity and improving semantic fidelity in low-resource medical NLP settings.