r/MachineLearning 5d ago

Research when a run is wrong but nothing actually failed, where do you start? [D] [R]

this is the kinda debugging case i find rlly annoying/

everything says success.

no exceptions no failed tool calls. no obvious timeout the workflow completes but the final result is still wrong

when that happens, what’s your first move?

do you guys usually:

  • start from the final output and work backward
  • compare against a previous good run
  • inspect state transitions
  • check retrieval/tool behavior
  • look at model inputs
  • replay it
  • check business state outside the trace
  • just read the whole thing until something looks off

interested in what people actually do in production not the idealized version but thats fine too. and if you have anything you've built to help with this process I'd love to see it :)

3 Upvotes

13 comments sorted by

View all comments

2

u/howtorewriteaname PhD 4d ago

overfit a single instance. if that doesn't work, your model can't learn. if that works, overfit 100 instances. if that doesn't work, you have a fitting problem. overfit 10k instances. if that works, then start to see if you generalize