Part 3/9:
An essential aspect highlighted is variety in training data. Since the goal is to "remove possibilities" and focus the model’s behavior, a broad spectrum of examples helps the model generalize better within the targeted task. The more diverse the input prompts, the better the model will perform in different scenarios resembling those examples.
Synthetic Data Generation Strategy
Shapiro advocates for synthetic data creation as a fast, cost-effective method to generate training datasets tailored to specific applications. His approach involves:
- Crafting detailed prompts that specify the structure and content of desired outputs—such as plot outlines, summaries, or narratives.