Training Data
Training data teaches the behavior you want by showing representative input-output examples.
Matches real use cases.
Examples follow the same target behavior.
Errors and contradictions are removed.
Your dataset is part of the specification
If examples disagree about tone, format or labels, the model receives a mixed signal. Dataset quality can matter more than simply adding more examples.
Collect examples from the real distribution of tasks you expect, including difficult but legitimate cases.
Quick check
Explain this lesson back in one sentence before continuing. If you cannot, reread the example and key takeaway.
Do not train on sensitive, copyrighted or otherwise unauthorized data merely because it is available.
Representative, consistent and authorized examples are the foundation of a useful fine-tuning dataset.