This research addresses a critical machine learning challenge by embedding domain-specific knowledge from LLMs into the feature engineering process, significantly reducing computational costs.

• Reduces random guessing in evolutionary computation approaches
• Leverages domain expertise from large language models to guide feature selection
• Improves efficiency of the machine learning pipeline
• Enhances model robustness through more intelligent feature engineering

For engineering teams, this approach offers a practical solution to one of ML's most resource-intensive tasks, potentially accelerating development cycles and improving model performance with fewer computational resources.

Embedding Domain-Specific Knowledge from LLMs into the Feature Engineering Pipeline