In this work, we implement a general framework and develop an open-source package to utilize LLMs for DSLs and apply it to the generation of input files for the quantum chemistry software package ORCA . Key to this framework is the development of three synthetic data generation schemes. Additionally, we demonstrate the effects of finetuning, prompt engineering, and retrieval-augmented generation (RAG) on the performance of GPT-3.5 Turbo. We find that finetuning, even on small synthetic datasets, significantly boosts performance, especially when combined with chain-of-thought prompting. Our best model significantly outperforms both the GPT-3.5 Turbo and GPT-4o baselines. We believe that our work builds a solid foundation for future improvements in the generation of ORCA input files specifically, and the area of DSL-synthesis for chemistry in general. Accordingly, our framework, which is transferable to any DSL, even outside chemistry, will help to improve the efficiency of researchers in the near future.