Part 3/11:
Why does this matter? Because if you want the model to know or recall facts from your data, fine-tuning is not the right tool. It’s better suited for developing models that perform specific tasks, such as text classification or pattern recognition.
Fine-Tuning vs. Search Technologies: A Side-by-Side Comparison
Fine-Tuning: The Challenges
Speed and Cost: Fine-tuning is slow, difficult, and expensive. Retraining or adjusting large models involves significant compute resources and time.
Scalability: The cost increases proportionally with the dataset size. Every new document or piece of data may require retraining.