words are often specified literally in the prompt. This poor performance supports the notion that LLMs are not well-suited for DSL code synthesis due to insufficient exposure in their pretraining. When we combine the poor base model with RAG, we observe a higher fraction of executable files compared to the GPT-3.5 Turbo baseline, even outperforming the GPT-4o base model in this regard. Intuitively, this makes sense as we provide external information about ORCA inputs including numerous functional examples. Using RAG also leads to slight improvements in the F1 scores. While improvements are insignificant for predicted options and settings, the additional context helped the model to improve keyword prediction, likely by referencing a broader range of valid keywords from the added information.