Large language models (LLMs) are increasingly used for automating complex tasks, even in specialized fields like data science. But are they truly up to the challenge? New research suggests LLMs might be oversimplifying things when it comes to tabular data, a core component of many machine learning problems. Think of tabular data as information organized in spreadsheets – rows and columns, like Excel files. Feature engineering, a crucial step in preparing this data for machine learning, involves creating new, informative features from existing ones. For example, combining 'age' and 'income' to create a new feature like 'spending power'. This process is traditionally time-consuming and requires expert knowledge. LLMs, with their vast knowledge base, seemed like the perfect solution to automate this. However, a study examining four major LLMs—two large, commercially available models and two smaller, open-source ones—revealed a concerning trend. When tasked with feature engineering on 27 different tabular datasets, the LLMs showed a bias towards simpler operations, like adding or subtracting features, while neglecting more complex and potentially insightful transformations. Imagine an LLM constantly choosing to just add two columns together, even when a more sophisticated combination might reveal hidden patterns. This bias towards simplicity negatively impacts the accuracy of machine learning models trained on these engineered features. While the smaller LLMs performed slightly better, none came close to the performance achieved by specialized automated feature engineering tools. This raises important questions about the suitability of LLMs for tasks requiring nuanced data manipulation. Are they relying too heavily on superficial patterns? Do they lack the deeper understanding needed to truly extract meaningful insights from tabular data? Further research is crucial to overcome this limitation. Techniques like prompt tuning or fine-tuning LLMs on specific data science tasks could help them learn to appreciate the complexity of feature engineering. Ultimately, the goal is to unlock the full potential of LLMs as automated data science assistants, allowing us to move beyond simple calculations and harness the true power of data.