Importantly, despite their proficiency in general-purpose code synthesis and expertise in fields like chemistry, LLMs tend to struggle with DSLs. We hypothesise that this is due to the specialized nature of DSLs and their limited coverage in the LLM's training data. While LLMs are trained on vast data, it consists predominantly of widely used languages and general concepts. In contrast, DSLs typically have limited documentation, little public code, and a small user base. This information scarcity could translate to poor performance on DSLs. Furthermore, the syntax and semantics of DSLs can differ significantly from popular languages like Python, making it challenging for LLMs to generalise their knowledge to DSLs.