RAG vs Fine-tuning

RAG (Retrieval-Augmented Generation) and fine-tuning are two approaches to enhancing language models, but they differ in methodology and use cases. Fine-tuning involves training a pre-trained model on a specific dataset to adapt it to a particular task, making it more accurate for that context but limited to the knowledge present in the training data. RAG, on the other hand, combines real-time information retrieval with generation, enabling the model to access up-to-date external data and produce contextually relevant responses. While fine-tuning is ideal for specialized, static tasks, RAG is better suited for dynamic tasks that require real-time, fact-based responses.