  In the advent of democratized usage of large language models (LLMs), there is
a growing desire to systematize LLM prompt creation and selection processes
beyond iterative trial-and-error. Prior works majorly focus on searching the
space of prompts without accounting for relations between prompt variations.
  Here we propose a framework, Prompt Exploration with Prompt Regression
(PEPR), to predict the effect of prompt combinations given results for
individual prompt elements as well as a simple method to select an effective
prompt for a given use-case. We evaluate our approach with open-source LLMs of
different sizes on several different tasks.
