Fine-tuning

Fine-tuning is the process by which an already pre-trained large language model is further trained on a specialized, smaller dataset to perform better in a specific domain or task type. This procedure modifies the model's weights, enabling the deep integration of corporate terminology, stylistic features, or a specific knowledge base. The advantage of fine-tuning over prompt engineering is that knowledge becomes internalized, reducing the length and cost of prompts while the model achieves expert-level precision in the targeted area.