Large Language Models Enable Feature Engineering for Tabular Data

A technical guide demonstrates how pre-trained large language models can extract structured features from unstructured text to support supervised classification tasks. The process involves transforming raw textual data into tabular formats that integrate with numeric columns for machine learning pipelines.

The methodology utilizes the Groq API to access Llama family models for inference. Developers interact with these models using the OpenAI client library, which provides a standardized interface compatible with various providers. This approach allows for the extraction of domain-specific signals, such as urgency and frustration levels, from customer support tickets. These extracted attributes are formatted as JSON objects, which are then converted into Pandas Series and merged with existing DataFrame columns to create a comprehensive dataset.

The demonstration employs a synthetic dataset of one hundred customer support tickets. Each ticket includes text descriptions alongside structured numeric features like account age and prior ticket counts. The class labels span several ticket categories, serving as the target for the classification model. The feature engineering step is designed to capture correlations between textual sentiment and specific ticket types, such as access lockouts or outages, which may be more urgent than general inquiries.

Practical considerations for production environments include managing cost and latency. The guide recommends batching requests to process multiple tickets per call, caching results based on stable identifiers to avoid redundant billing, and implementing retries with backoff to handle transient network errors or rate limits. These practices aim to improve pipeline reliability and efficiency.

The final stage involves training and evaluating a random forest classifier on the engineered dataset. The data is scaled and split into training and test subsets. The guide notes that results on the small, partly random synthetic dataset may show limited accuracy, precision, and recall. This outcome is attributed to the small sample size rather than the feature engineering process itself. The core objective is to validate the reliability of using LLMs to convert raw text into meaningful tabular features for downstream modeling.