  The representation of feature space is a crucial environment where data
points get vectorized and embedded for upcoming modeling. Thus the efficacy of
machine learning (ML) algorithms is closely related to the quality of feature
engineering. As one of the most important techniques, feature generation
transforms raw data into an optimized feature space conducive to model training
and further refines the space. Despite the advancements in automated feature
engineering and feature generation, current methodologies often suffer from
three fundamental issues: lack of explainability, limited applicability, and
inflexible strategy. These shortcomings frequently hinder and limit the
deployment of ML models across varied scenarios. Our research introduces a
novel approach adopting large language models (LLMs) and feature-generating
prompts to address these challenges. We propose a dynamic and adaptive feature
generation method that enhances the interpretability of the feature generation
process. Our approach broadens the applicability across various data types and
tasks and draws advantages over strategic flexibility. A broad range of
experiments showcases that our approach is significantly superior to existing
methods.
