Currently, large language models (LLMs) are increasing in popularity in the context of code synthesis . Most such LLMs are causal decoding-only models built on the transformer architecture . They do not generate output from a fixed encoded representation but instead sequentially, predicting the next token based on the context of the previous tokens. The transformer blocks, which consist of layers of multi-head self-attention and feed-forward neural networks, enable the model to dynamically focus on different parts of the input, informing the prediction of each subsequent token by relevant portions of the preceding text. Hence, they have to be fed with text, referred to as a prompt, to be continued. In case of code synthesis, this prompt would generally consist of NL instructions on how the code should look or function. Recently released LLMs, such as GPT-4 and Gemini 1.5 , have shown remarkable capabilities in code synthesis, translating NL instructions into functional code for popular programming languages (PLs) like Python and Java . These models are trained on vast and diverse datasets and can solve complex tasks given appropriate prompts. Additionally, their chat-like interface provides interactivity to users seeking clarification, troubleshooting, or deeper understanding. Accordingly, they have the potential to lower the barrier of DSLs significantly, thereby boosting overall research productivity.