It is crucial to exercise caution when incorporating LLMs into data science workflows, particularly with previously unseen data or in unfamiliar domains. Data science at its core involves a comprehensive understanding of data within its unique context. LLMs, while capable of generating functional code and providing insightful suggestions, do not inherently comprehend the underlying implications of the data they process. This disconnect can introduce biases in methodology and potentially lead to a misinterpretation of the problem. To mitigate these risks, it is advisable to limit the use of LLMs to labor-intensive, basic data wrangling tasks. Even then, sensitive data should not be directly fed into the LLM. Instead, use dummy examples or limited portions of the data to guide the LLM without compromising data integrity. This approach depends heavily on understanding the intricate relationships between features and adhering to data protection requirements. However, be aware that strippin...