The understanding of the structural changes made by functional group additions or other transformations can provide insights into how the model interprets and responds to the prompts, which could be beneficial for using LLMs in rational molecular design and molecular optimization. While some may argue that inverse molecular design is the ultimate goal, understanding how LLMs function, perform inverse design, and comprehend molecules and chemistry is essential. To fully capitalize on the potential of LLMs in molecular design, further research is needed to better understand the design space, role of prompt engineering, and unravel the underlying mechanisms by which these models navigate the chemical space and generate molecules with (conditional generation with targets) or without (generation with no targets) desired properties.