Part 3/13:
Pre-trained models like GPT, OpenAI, Azure, and others are trained on vast corpora of data. They generally offer sufficient capability for many applications through prompt engineering, which involves crafting prompts to elicit desired responses. However, in domain-specific contexts, these base models may lack the precision required.
Fine-tuning involves taking this base model and modifying select parameters using domain-specific data—called training data—to produce a specialized model tailored for particular use cases. This process enables models to understand nuances and deliver accurate results aligned with specific business needs.
The Fine-Tuning Pipeline: From Preparation to Deployment
The speaker emphasizes a structured approach: