Building a Training Dataset

Dataset

Part of: Fine-Tuning & Evals

You finally decided fine-tuning is worth it (lesson 1 told you when). Now comes the part that actually determines whether it works: the dataset. Teams obsess over which base model to pick and then feed it 5,000 sloppy, contradictory examples, and wonder why the fine-tune is worse than the prompt they started with. The model can only learn the patterns you show it. Garbage examples teach garbage habits, faithfully. What it is A training dataset for fine-tuning is a list of input→output examples that demonstrate the exact behavior you want baked into the weights. Each example is a pair: the input the model will see, and the ideal output it should produce. For a chat-style fine-tune each row is usually a small JSON object, a prompt and the gold completion: The model doesn't learn what you tell it to do; it learns what your examples show it doing, over and over. So the dataset is the spec. If the spec is inconsistent, the learned behavior is too. How it works Three properties decide whether a dataset teaches the right thing: - Quality over quantity. 300 clean, consistent examples beat 3,000 noisy ones. Every wrong or sloppy label is a lesson the model dutifully memorizes. One mislabele

Challenge: The Dataset Auditor