The Sequence Knowledge #512: RAG vs. Fine-Tuning
Exploring some of the key similarities and differences between these approaches.
Today we will Discuss:
The endless debate of RAG vs. fine-tuning approaches for specializing foundation models.
UC Berkeley’s RAFT research that combines RAG and fine-tuning.
💡 AI Concept of the Day: RAG vs. Fine-Tuning
RAG vs. fine-tuning is one of the most common debates among teams building generative AI applications. That seems like a great topic to conclude our series about RAG.
Retrieval-Augmented Generation (RAG) and fine-tuning are two distinct approaches to enhancing the performance of large language models (LLMs), each with its own set of advantages and drawbacks. RAG dynamically incorporates external knowledge into the model's responses, while fine-tuning adjusts the model's internal parameters for specific tasks.