My Tangle with Debugging AI Stuff
Okay, so let’s talk about this thing I’ve been calling ‘AI Step Over’ in my head. It’s not like some fancy new tool I found, more like a way I started dealing with the mess that is debugging some of these AI pipelines.
I was working on this project, right? Trying to get some data ready for a model. You know the drill: cleaning messy text, turning categories into numbers, scaling things. Standard stuff, but the pipeline got really long. Lots of little steps using libraries like Pandas and Scikit-learn.
Now, when things went wrong, trying to debug was a nightmare. The standard debugger, you know, ‘step into’, ‘step over’ line by line? It just wasn’t cutting it. I’d step into some library function and suddenly I’m looking at code I didn’t even write, pages and pages of it. Or I’d ‘step over’ a single line that called a complex transformation, and boom, the data looks completely different, but I skipped right over why. I’d lose hours just trying to figure out which major part of my pipeline messed things up.
Trying to Make Sense of It
I got super frustrated. I kept thinking, I don’t care about the tiny details inside the scaling function right now. I just want to run the whole scaling part and see if the output makes sense. Then, if it’s okay, I want to run the whole feature encoding part and check that. Like a ‘step over’ but for a whole logical chunk of the AI process.
So, what did I do? I started changing how I wrote my code. It was kind of a pain, but it helped.
- I broke down my big pipeline script into smaller, really distinct functions. Like `load_data()`, `clean_text_column(data)`, `encode_categorical_features(data)`, `scale_numerical_features(data)`.
- Each function took the data, did its one job, and returned the modified data.
- Then, when debugging, instead of stepping line by line through the main script, I’d put a breakpoint after calling `clean_text_column(data)`, for example.
- I’d hit ‘run’ or ‘continue’ to execute that whole function in one go.
- Then I’d stop and inspect the ‘data’ variable to see if the text cleaning worked as I expected.
- If it looked good, I’d move my breakpoint to after the next major function call, `encode_categorical_features(data)`, and run again.
It wasn’t a real ‘AI Step Over’ button in my editor, obviously. It was just me, manually executing chunks of code between breakpoints. But it gave me that feeling of stepping over the big blocks.
Did it Work? Yeah, Kinda.
Honestly, it made finding high-level problems way faster. If suddenly my numbers turned into NaNs after the scaling step, I knew the problem was somewhere in that `scale_numerical_features` function, or maybe the data going into it was already bad. I didn’t waste time stepping through the text cleaning code if the error happened way later during scaling.
It wasn’t perfect, though. Sometimes the bug was buried deep inside one of those functions, and I’d still have to dive in line-by-line eventually. But this approach helped me isolate which function to dive into, which saved a ton of time overall.
It just made me think, we need better tools for this stuff. Debugging data transformations and model layers isn’t quite the same as debugging traditional application logic. You often care more about the state of the data between major steps. My little breakpoint trick was a workaround, born out of frustration, but it showed me what I really wanted: a way to treat those big AI/ML steps as single units during debugging. An ‘AI Step Over’, for real.