Comparison between RAG and fine-tuning
RAG and fine-tuning are often compared and considered techniques in opposition. Both fine-tuning and RAG have a similar purpose, which is to provide the model with knowledge it did not acquire during training. In general, we can say that there are two types of fine-tuning: one directed at adapting a model to a specific domain (such as medicine, finance, or other) and one directed at improving the LLM’s ability to perform a particular task or class of tasks (math problem solving, question answering, and so on).
There are several differences between fine-tuning and RAG:
- Knowledge updates: RAG allows a direct knowledge update (of both structured and unstructured information). This update can be dynamic for RAG (information can be saved and deleted in real time). In contrast, fine-tuning requires retraining because the update is static (impractical for frequent changes).
- Data processing: Data processing is minimal for RAG, while...
