Mining Hidden Prompt Engineering Patterns with Formal Concept Analysis and Association Rules

Abstract

Designing effective prompts to guide generative artificial intelligence (GAI) systems, or prompt engineering, has become a crucial skill. However, the underlying prompt patterns have not yet been thoroughly examined. This paper introduces a novel analytical method that combines formal concept analysis (FCA) and association rule mining. This approach is used to systematically analyze prompt engineering behaviors within an empirical dataset of human–AI interactions. Findings reveal hidden prompt patterns linking prompts to GAI outputs, providing insights that traditional analyses cannot offer. Furthermore, we demonstrate that prompting guides, especially those with examples, facilitate more sophisticated prompt engineering behavior and improve GAI output quality. Our work contributes to information systems theory by demonstrating the value of FCA-based structural analysis in human–GAI contexts and to the practice of prompt engineering by offering evidence-based guidance on improving prompt design and prompt engineering skill development.