Knowledge distillation (KD) compresses large teacher models into smaller student models by transferring soft labels or intermediate activations. While effective in general domains, KD alone falls short in specialised machine translation (MT) settings, such as biomedical translation. The student inherits only the teacher’s compressed knowledge and lacks access to external domain information. Moreover, standard KD typically relies on abundant parallel data, which is often unavailable in domain-specific scenarios. To address these limitations, we combine KD with retrieval-augmented generation (RAG) in a few-shot setting. We propose a retrieval-augmented enhanced few-shot KD framework for French-to-English biomedical translation task. The student learns to retrieve relevant in-domain knowledge from an external database, complementing the teacher’s supervision. We design and compare several retrieval strategies to enhance student capacity. Experiments show that with our terminology-aware retrieval-based methods, the student achieves performance comparable to or better than the teacher, while preserving translation quality and efficiency.