u/Enough_Hippo1359

I’ve been thinking about a pretty unglamorous part of LLM usage:

using them to help with repetitive data prep before the actual analysis starts.

Things like:

• pulling structure out of semi-structured files
• normalizing tables
• combining repeated inputs
• getting to a cleaner downstream starting point

What I keep coming back to is that the useful part isn’t full automation.

It’s reducing the repetitive setup work while still keeping verification in the loop.

In my own use, that’s felt most helpful when:

• the target output is stable
• the inputs vary a little, but not wildly
• human review is still part of the process

Curious how people here think about that boundary.

Where have LLMs actually been reliable for this kind of prep work, and where do you still refuse to trust them without tight review?