Build Your Own Dataset with Knowledge Distillation
Use a powerful LLM as a 'teacher' to automatically label raw data and create custom datasets for training and evaluating specialized models.
The most powerful AI systems aren't built on public datasets. They're built on custom data that matches the problem you're solving. When you build such a system, you might not have such dataset lying around. Maybe you need financial sentiment analysis, but the public datasets are too generic or your internal data is not labeled by humans.
This tutorial shows you how to create your own labeled dataset using Knowledge Distillation1. You'll use a powerful "teacher" LLM (like Gemini 2.5 Flash) to automatically label raw, unstructured data. The result will be a custom dataset that you can use to train smaller, faster "student" models specifically for your task.
We'll build a practical example - a sentiment analysis dataset for financial news. You'll start with raw articles about tech companies and end up with a cleanly labeled dataset ready for model training or evaluation.
Tutorial Goals
- Transform 1,000+ raw news articles into a clean, labeled sentiment dataset
- Build an automated labeling pipeline that you can apply to any text data
- Engineer prompts that produce consistent labels for your task
- Create a dataset ready for training your own specialized model