RAG vs. Fine-Tuning: Which Strategy is Best for Customizing LLMs?

Large Language Models (LLMs) have transformed interactions with technology, but they face challenges with domain-specific prompts and fresh information. To address this, Retrieval-Augmented Generation (RAG) and fine-tuning are two methods that enhance LLM adaptability. RAG operates by retrieving external data during inference, akin to an open-book test, while fine-tuning involves retraining a model on a specialized dataset, embedding specific knowledge directly. A recent approach, RAFT (Retrieval-Augmented Fine-Tuning), merges these methods, integrating retrieval and generative processes to improve accuracy and adaptability in domain-specific tasks. RAG is ideal for current information needs, fine-tuning excels in specialized applications, and RAFT offers a comprehensive solution by combining the strengths of both.

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