Can we use LLMs to automate the exploratory data analysis phase of our projects safely?

All answers to this question.

It's an incredible productivity booster, but you must be the "Lead Pilot." LLMs are excellent at generating the boilerplate code for Matplotlib or Pandas that used to take us 20 minutes to look up on Stack Overflow. However, in late 2023, we noticed that if the data had null values in a specific way, the AI-generated code would sometimes silently drop important rows without warning. Use the LLM to write the "Draft" of your EDA, but you must manually verify the logic. It’s a tool for speed, not a replacement for statistical rigor.

   Answered 2025-09-25 by Heather Bennett

Do you find that using these AI tools makes it harder for junior team members to actually learn the underlying syntax of the libraries?

   Answered 2025-09-26 by Patrick Sullivan

• That is a major concern for the future of mentorship. If a junior dev just copies and pastes AI code, they don't develop the "muscle memory" for debugging. We've implemented a rule where juniors can use AI for ideas, but they have to explain every line of the code they submit. It ensures they aren't just "cargo culting" the output. The goal is to use the AI to augment their understanding, making them 10x faster without becoming 10x more dependent on the machine for basic tasks.

     Commented 2025-09-27 by Russell Peterson

LLMs are great for "Rubber Ducking." I often paste my own code and ask the AI to find potential edge cases I might have missed in my analysis.

   Answered 2025-09-28 by Raymond Jenkins

• That’s a brilliant way to use it, Raymond. Using it as a reviewer rather than a creator is much safer and highly effective.

     Commented 2025-09-29 by Pamela Reed