While our best-performing model is still insufficient for an end user product, our study is a first-of-its-kind and paves a clear path towards this goal. Accordingly, there are several limitations to be addressed in future research. We believe that the most promising avenue is increasing the size and diversity of synthetic datasets. Additionally, applying our methodology to open-source LLMs, perhaps even models specialized on code synthesis, would allow to implement a meaningful loss function. Furthermore, exploring advanced RAG techniques, which incorporate ranking mechanisms, or integrating context during finetuning, presents a promising future avenue.