Large language models (LLMs) are becoming ubiquitous in engineering and science because they can turn prompts into data analysis code, experimental designs, formulations of optimization problems, among other applications. However, many LLMs suffer from a phenomenon called order dependency, in which the order of phrases in the prompt affects their performance on a given task. To overcome this issue, we introduce a systematic method that uses order-of-addition experiments to quantify the ordering effect of elements in a prompt and identify their best positions. We demonstrate our methodology by constructing two-level fractional factorial designs using state-of-the-art LLMs. We show that order-of-addition experiments can elucidate order dependency in these LLMs, and can help us to identify a high-quality prompt configuration for the task.

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