Building a Training Set That Works

Fine-tuning projects rarely fail on the training run. They fail on the dataset, and usually in ways that are visible before training if anyone looks.

Quality decides the ceiling

A few hundred carefully reviewed examples outperform tens of thousands scraped together. The model learns the patterns in what you give it, including the inconsistencies, so contradictory examples of how to handle the same situation teach it to be inconsistent.

Balance and coverage

If ninety percent of examples are one category, the model will over-predict that category. Check the distribution deliberately and include the rare cases you care about at a higher rate than they occur naturally, then evaluate on a realistic distribution.

Edge cases are the point

The common case usually works without tuning. Ambiguous inputs, requests that should be refused, malformed data and out-of-scope questions are where behaviour actually needs shaping, and they are what teams leave out because they are harder to label.

Hold out a genuine test set before training and never look at it while iterating. A dataset split after the fact is how teams convince themselves a model is better than it is.