Part 4/7:
Fine-Tuning: Unlocking Proprietary Data and Cost Savings
Fine-tuning offers several strategic benefits:
Utilizing Proprietary Data: Companies with vast datasets unique to their operations can adapt LLMs to better reflect their knowledge base.
Cost Reduction: Once an effective model is fine-tuned, it can replace more expensive, complex models, resulting in lower latency and operational costs. For example, fine-tuning a smaller model to replicate a larger one’s performance allows businesses to save on computational expenses.
Making Fine-Tuning Accessible and Cost-Effective
Historically, fine-tuning has been resource-intensive—costly and time-consuming—limiting widespread adoption. However, recent advancements are changing the landscape: