We make use of proven prompting techniques without a central prompt engineering framework. We instruct the model to behave as a chemistry expert , describe the format of an ORCA input file, and employ few-shot learning by showing five examples of prompt and input file pairs . While this number of examples is arbitrary, including too many examples can lead to a performance decline . This system prompt serves as the basis for the other, more elaborate prompts. CoT (Chain-of-Thought) : This prompt defines five linear steps to gather the key information. The model is instructed to type the answer to all these steps, clearly separating which part of the user prompt refers to which part of the input. CoVe (Chain-of-Verification) : The model first needs to make an initial guess of the correct input file. Next, we perform the same five steps as in CoT. However, instead of reasoning what terms should be used, we ask the model to verify if the initial choices are correct or changes are required. GoT (Graph-of-Thought) : This prompt instructs to use a nonlinear way of mapping its reasoning. It encourages to find the core elements of the user prompt and to create connections between them. For every element, multiple possibilities should be considered to consolidate the most suitable choices. ToT (Tree-of-Thought) : The model is instructed to explore different branches of thought and to consolidate them in a final answer. In the first step, the model writes down multiple ideas for what the ORCA input should be.