Exploring a codebase tells you WHAT it's doing, but not WHY. In older codebases you'll often find weird sections of code that solved a problem that may or may not still exist. Like maybe there was an import process that always left three carriage returns at the end of each record, so now you got some funky "lets remove up to three carriage returns" function that probably isn't needed. But are you 100% sure it's not needed?

Same story with data models, let's say you have the same data (customer contact details) in slightly different formats in 5 different data models. Which one is correct? Why are the others different?

Ultimately someone has to solve this mystery and that often means pulling people together from different parts of the business, so they can eventually reach consensus on how to move forward.

> creating documentation

How is an AI supposed to create documentation, except the most useless box-ticking kind? It only sees the existing implementation, so the best it can do is describe what you can already see (maybe with some stupid guesses added in).

IMHO, if you're going to use AI to "write documentation," that's disposable text and not for distribution. Let the next guy generate his own, and he'll be under no illusions about where the text he's reading came from.

If you're going to write documentation to distribute, you had better type out words from your own damn mind based on your own damn understanding with your own damn hands. Sure, use an LLM to help understand something, but if you personally don't understand, you're in no position to document anything.

I don't find this surprising. Code and data models encode the results of accumulated business decisions, but nothing about the decision making process or rationale. Most of the time, this information is stored only in people's heads, so any automated tool is necessary blind.

Please don't feed people LLM generated docs