You now know how to prepare data that produces reliable fine-tuning results.
Key takeaways:
- Quality beats quantity every time
- Use the right format for your base model (Alpaca, ShareGPT, ChatML)
- Always maintain train/validation splits
- Clean and filter aggressively
- Synthetic data bootstraps when real data is scarce
- Version your datasets
Next, I'll show you how to run supervised fine-tuning with this prepared data.