On building self-sufficient ML pipelines

I've been thinking a lot about coding self-sufficiency lately. The temptation, especially when working on something like a CNN pipeline at 11pm on a Tuesday, is to ask Claude to generate the whole thing and move on. The code works. The deadline is met. But six months later, when I look at my own repo, I can't tell you why a particular preprocessing step exists.

So I've been changing how I work.

The rule I've been trying to keep is: if I can't explain why a line of code is there, it doesn't go in. That means writing more from scratch, asking the AI for explanations rather than implementations, and keeping a separate file of "things I asked about and now understand."

It's slower. It's also the only way the work actually compounds.

What I've changed

For my image authenticity classifier project, I now write the dataset preparation script myself, end to end, before reading any reference code. If I get stuck, I describe the problem in plain English first, then write what I think the solution should look like, then check it against an LLM. The error correction is where most of the learning happens.

For the modeling code, I'll often ask Claude to walk me through why a particular architecture choice makes sense, what trade-offs it implies, and what it would look like to choose differently. Then I implement.

This approach isn't anti-AI. It's anti-passivity. The tool is incredible. The trap is letting it do thinking that should be mine.