RAG vs. Fine-Tuning: Which Is Best for Your LLM?

Large Language Models (LLMs) have transformed technology interactions but often struggle with domain-specific prompts and new information. To address this, Retrieval-Augmented Generation (RAG) and fine-tuning offer distinct solutions. RAG enhances LLM knowledge by retrieving external information during inference, ensuring responses are current and contextually accurate, while fine-tuning involves retraining a model on specific data to embed specialized knowledge. A recent approach, RAFT (Retrieval-Augmented Fine-Tuning), developed by UC Berkeley, combines the strengths of both RAG and fine-tuning to create a more effective training strategy, particularly for domain-specific tasks, by integrating retrieval and generative processes. RAG is ideal for tasks requiring up-to-date information, fine-tuning provides in-depth expertise for specialized applications, and RAFT offers a comprehensive approach by improving accuracy and reasoning capabilities. Choosing the right method depends on specific needs, with RAG, fine-tuning, and RAFT each presenting unique advantages.