I'm curious about how artificial intelligence systems like large language models are trained—what data and methods are used? What are the fundamental principles behind this process? How much human intervention is required, and how is data quality measured? Could you explain?
How are AI models trained?
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The other day, while trying to integrate ChatGPT into a small IoT project, I got to thinking—how is it that this thing can talk so intelligently on its own? I thought, "What if I collect real-time usage data from my smart lights in Bakırköy and feed it into a language model?" That is, until it started giving me ridiculous suggestions like, *"I wonder what you’d drink on the balcony when it’s sunny."*
Then it hit me: the model isn’t really learning from my tiny dataset—it’s fueled by billions of people’s chatter.
Let me explain with a little story: Last year, I wrote a Python script to optimize my grocery shopping, basically trying to reverse-engineer the supermarket’s pricing algorithm. I manually entered prices for veggies I bought from the greengrocer, discount dates, and so on. A few weeks later, the script suggested, *"Dude, avocados are cheap today—maybe freeze some?"* That’s when I realized my data was way too limited. Once I started using the store’s real data, it actually worked.
The truth is, large language models are just like my grocery script—but trained on millions of times more and cleaner data. That data usually comes from web scraping, books, academic papers, and the like. To measure quality, they test the model on "unlabeled data" and see how accurate its predictions are. Human intervention comes in to clean up bad data and retrain the model. So yeah, it’s a bit ironic—we’re all volunteering our data to feed these systems.