Fine-Tuning vs. RAG

Balancing Cost and Precision

With the rapidly advancement in artificial intelligence, choosing the right approach for customizing pre-trained models for your business need is crucial. The two prominent techniques often considered are fine-tuning and Retrieval-Augmented Generation (RAG). The choice of the techniques is not just technical, but also involves cost and business considerations. In this post, I prepare the considerations in terms of cost, speed, and outcome accuracy for your group to consider.

Aren’t pre-trained models already trained on a vast amount of data? Why should we even talk about fine-tuning a pre-trained data? In case you’re wondering why we apply fine-tuning or RAG to pre-trained models, here are the reasons. Pre-trained models are trained on extensive datasets available up to a certain point in time. This means they might not be aware of any headline news or developments that occurred after their training period. Additionally, pre-trained models may lack specialized domain knowledge, such as details about proprietary products or the latest advancements in technical fields. Fine-tuning and RAG techniques help the model learn the specific terminology and nuances of these areas, thereby improving its accuracy and relevance.

(1) Still, Let’s Understand Fine-Tuning and RAG First