My main workflow improvement over the course of my PhD has been making my papers reproducible. This means: I clone a github repo, then I execute a single command to re-run all computations, re-generate all figures and tables, and compile the final paper.

Any manual step like "resize the figure window until the proportions look good", or "run script A, then run script B", or "upload this file to Overleaf" creates an opportunity for your results (figures and tables) to become out of sync with your source code. This can cause big problems.

I use Makefiles, Anaconda, and Github CI to accomplish this [1], but the techniques don't matter. The point is the end goal.

More broadly, in research it's always tempting to do things quick and dirty. Conference deadlines loom. Code bases are usually small and short-lived. But in my experience, your 4-months-later future self will appreciate tests, documentation, etc. just as much as your 4-years-later future self.

You can still be quick and dirty when it comes to project scope. This is where you get most of the speedup anyway. Go ahead write a function that doesn't handle edge cases, but add a comment about it!

[1] https://github.com/jpreiss/reproducible_papers