Part 8/9:
- Saving prompts and responses separately, consistently naming files for easy correlation.
He recommends debugging prompts by printing outputs and inspecting the dataset before proceeding to fine-tuning, thus avoiding wasted tokens on irrelevant data.
Future Directions: Data Augmentation and Enhancement
In wrapping up, Shapiro teases further tutorials on data augmentation, cleaning, and advanced fine-tuning techniques. He mentions possibilities like:
Using the edit endpoint to expand or refine training data.
Combining multiple augmentation steps to create highly tailored datasets.
This iterative process ensures models can be fine-tuned to perform complex tasks, from detailed storytelling to domain-specific responses.