The main benefits of fine-tuning are that it leverages the pre-trained language knowledge of the LLM as well as adding on the more domain specific knowledge. So, now the LLM can generate responses that are more relevant to the company. Further, since fine-tuning is only done for the last few layers, the LLM can effectively learn the nuances of the new task as these layers will be more specific to the given task.
However, fine-tuning is a slow, expensive, and risky process. It requires canada whatsapp number data significant computational power and an expert team to carry it out. Additionally, managing the model becomes problematic when information or the source data changes, needing a repetition of the entire expensive and slow process. effective for adjusting the consistent behavior of the model (e.g., answering questions in a chat style, generating code, etc.) rather than regularly updating the model’s knowledge.
To address these challenges, researchers from Meta/Facebook developed the RAG (retrieval augmented generation) approach. RAG allows for more accurate and context-sensitive responses by integrating retrieval mechanisms with generative models.