Part 2/9:
Shapiro begins by emphasizing that models like GPT-3 are already extensively trained on massive datasets, making traditional training unnecessary for most custom tasks. Instead, fine-tuning is about narrowing the model’s capabilities to perform a specific task consistently and accurately. This is achieved by providing the model with clear, diverse examples that reinforce the desired behavior.
Key Point: Fine-tuning isn't about teaching the model new information but about guiding its responses more reliably towards a particular output or style.