LLM Classification Is Feature Engineering

The article explores the evolving role of Large Language Models (LLMs) in modern data pipelines, arguing that LLM-based classification should be viewed primarily as a sophisticated form of feature engineering rather than a standalone solution. By leveraging LLMs to extract structured features from unstructured text, developers can feed these high-quality inputs into traditional, more interpretable, and computationally efficient machine learning models. The author contends that this hybrid approach—using LLMs for semantic understanding and classical models for decision-making—often yields better performance and cost-effectiveness than relying solely on massive models for end-to-end classification. This perspective shifts the focus from treating LLMs as black-box classifiers to utilizing them as powerful tools for data transformation and enrichment, ultimately bridging the gap between cutting-edge generative AI and robust, production-ready software engineering practices.