Feature Engineering Still Outperforms Raw Data Inputs, Even in the LLM Era

A software engineer argues that the widespread belief that large language models can extract meaningful patterns from raw, unprocessed data is flawed and costly in production environments. Using e-commerce churn prediction as a case study, the author demonstrates that feeding raw columns like purchase dates and order counts directly to a model yields poor results because the model lacks the contextual reasoning to interpret them correctly. Applying classical RFM (Recency, Frequency, Monetary) feature engineering — such as converting last purchase dates into days-since-last-purchase and normalizing order counts by account age — consistently delivers greater accuracy gains than switching algorithms. The author acknowledges that LLMs do play a role in feature engineering, particularly in converting unstructured text like customer reviews into structured sentiment signals, but stresses that the model is merely a tool. The core argument is that deliberate, thoughtful feature design remains the most impactful lever in machine learning pipelines, regardless of model size or architecture.