Mining Hidden Prompt Engineering Patterns with Formal Concept Analysis and Association Rules
Journal
Proceedings of the 59th Hawaii International Conference on System Sciences
Type
conference paper
Date Issued
2026-01-09
Author(s)
Research Team
IWI6
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.
Language
English
Keywords
Prompt Engineering
Prompt Pattern
Human-AI Interaction
Formal Concept Analysis
Prompting Guide
HSG Classification
contribution to scientific community
Refereed
Yes
Publisher place
Maui, Hawaii, USA
Pages
10
Event Title
Proceedings of the 59th Hawaii International Conference on System Sciences
Event Location
Maui, Hawaii, USA
Event Date
06.01.-09.01.2026
Subject(s)
Division(s)
File(s)![Thumbnail Image]()
Name
JML_1049.pdf
Size
572.99 KB
Format
Adobe PDF
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