Just realized my AI training data was the problem, not the model

I spent 3 weeks in Austin trying to fine-tune a chatbot for ticket routing and kept getting garbage replies. Turns out my export from Zendesk had 40% duplicate and outdated entries, and the model was learning the noise. I cleaned it with a simple Python script that dropped anything older than 18 months and removed exact matches, and accuracy jumped from 61% to 84% overnight. Has anyone else found that their source data quality matters way more than the actual algorithm choice?

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johns1811d ago

Haven't we all been there, honestly? I remember spending a whole weekend cleaning up a training set for a support bot that kept telling people to "reboot their coffee maker" because some outdated forum posts got scraped in. The irony is I spent weeks reading about attention layers and learning rates, but the real fix was just deleting 15,000 rows of junk. It's like finding out your fancy sports car has been running on flat tires the whole time, you know? Once I filtered out all the duplicate tickets from people who submitted the same issue three times, the thing finally started making sense. Makes me wonder how many failed AI projects out there are really just crappy spreadsheets in disguise.