The original title is "Feature engineering didn't die. Engineers just stopped doing it"

Short summary

Feature engineering remains critical for ML success despite the LLM trend; thoughtfully engineered features outperform raw data regardless of model choice. The author demonstrates with a churn-prediction example using RFM pattern, showing how engineered features let models reason meaningfully. This principle applies to LLMs, XGBoost, and neural networks alike.

• •Good features matter more than model complexity; raw data alone doesn't cut it
• •RFM pattern engineering (Recency, Frequency, Monetary) outperforms naive numeric encoding
• •LLMs and traditional models both benefit from feature engineering—it's still thinking work

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