You can't rely on LLMs to understand the grain of your data (www.bioinformaticszen.com)

A recent article highlights the limitations of using large language models (LLMs) for data analysis, particularly in contexts like SQL queries where understanding the "grain" of data is crucial. The author points out that while LLMs can generate results quickly, they sometimes miss subtle issues that analysts originally catch during manual processes. For instance, in an example using e-commerce data, an LLM correctly identified a fan-out issue when joining order and payment data, but its suggested fix led to another error in revenue attribution. This reflects a broader cognitive risk: reliance on LLMs can foster an illusion of correctness in the generated results, which may deter deeper scrutiny by analysts. The implications for the AI/ML community are significant as this scenario underscores the need for critical thinking and verification when using LLMs for complex data analysis tasks. The author suggests incorporating a "planning mode" that encourages analysts to engage in thoughtful review and question generation, potentially bridging the gap between the LLM’s rapid output and the analyst's deeper understanding. As LLMs evolve, addressing their ability to handle such intricacies will be vital not only for improving their accuracy but also for minimizing cognitive biases in data analysis workflows.