Spent 2 years fine-tuning prompts before I realized the AI was just guessing based on bad data

I was chasing perfect results until a colleague showed me our training dataset had 40% garbage in it, and now I wonder how many of these innovations are just polished nonsense.

3 comments

3 Comments

fiona_west2122d ago

Yeah that "polished nonsense" part hits close to home. I used to think all these AI tools were magic until I actually looked under the hood at work. We had a chatbot that was supposed to handle customer complaints but it kept suggesting people reboot their routers for billing questions. Turns out someone tagged the training data wrong and the AI learned that "payment issue" meant "internet problem." Now I run a quick sanity check on any dataset before trusting the outputs. It's amazing how much bad data gets passed off as smart technology.

ray_martinez8222d ago

Man that "reboot your router for billing questions" bit really got me. It's like the AI learned one specific pattern and just ran with it completely off the rails. What gets me is how people think because it sounds confident and polished that it must be right. I've seen it happen with our team too where someone spends weeks training a model on data that looks clean but has some underlying bias nobody caught. Then the output looks great on paper but makes zero sense in practice. It's like that saying about garbage in garbage out but people forget that part when they see the fancy interface. You gotta check the data yourself even if it takes extra time because these tools will happily spit out the wrong answer with total confidence.

jason_lewis322d ago

Wait, wait wait, hold on @fiona_west21 - you're telling me someone actually tagged "payment issue" as "internet problem" in the training data? That's such a basic mixup but it explains everything about how these things go sideways. I mean I get how it happens - someone rushing through tagging thousands of rows and boom, your whole model thinks every billing complaint is a connectivity issue.