The LLM's ability to consistently return valid and canonical SMILES with a simple molecular representation like SMILES demonstrates its robustness and reliability in generating molecular structures. However, many factors may contribute to the occasional failure to generate valid molecules. One issue is the phenomenon of hallucination by LLMs, which arises from sometimes competing objectives to balance following detailed instructions in the prompt and obeying grammatical rules for SMILES. Additionally, generating valid molecules in a zero-shot setting, as done in our work, is inherently more challenging than in a fewshot approach or an iterative improvement scheme. In a zero-shot setting, the LLM is required to generate a response without any prior examples being given or feedback to the response generated, meaning it only gets one attempt to complete the specified task. This contrasts with a few-shot approach, where the model is provided with a few examples, or an iterative improvement scheme, where the model can refine its predictions over multiple iterations. The use of a zero-shot approach instead of a few-shot or iterative refinement significantly increases the difficulty of the molecular design problem. Another factor contributing to imperfect validity rates is the occasional copying of the parent SMILES when asked to generate new molecules or repeated generated of the same SMILES in one query, leading to duplicate structures. These duplicates and copies of parent SMILES are eventually removed during filtering, decreasing the overall validity ratio.