Research taste is a skill nobody talks about. How do you develop it without collaborators? [D]

if you’ve ever built an elegant, complex ML pipeline to solve something a 10-line prompt could’ve handled… this is for you.

i’ve been thinking about what separates people who do useful research from people who do impressive-looking research. it’s almost always the problems you choose rather than raw technical skill.

here’s the mental model i’ve landed on. every problem kind of follows these steps:

1. find a clear problem people actually care about
2. try the dumbest solution first. can a simple prompt solve this? if yes, you’re done
3. if not, now you get to think about a research solution
4. if that’s too hard right now, scope down. what subset of the problem can you actually solve?

research taste is all about not getting led off a) solving simple problems using complex solutions, or b) getting stuck on a tough problem that the field isn’t ready for yet.

the hard part is that taste usually gets built through friction. a good advisor who pushes back, a collaborator who asks “wait why can’t you just…”, reviewers who call out overcomplicated baselines. a lot of us don’t have that.

so for people doing empirical research with limited collaborators, how do you keep yourself honest? any tips or tricks on not over-engineering solutions, knowing when a problem is worth pursuing, knowing when to scope down vs push through? would love to hear what’s actually worked for people rather than textbook answers.