Feature engineering still matters as ML teams skip it for LLMs

Many teams now skip feature engineering, assuming large models will learn patterns from raw data. But a logistic regression with well-crafted features often beats a neural network on messy data. For churn prediction, raw columns like last_purchase date fail to capture recency, while engineered features like days since last purchase improve accuracy.