RAG vs Fine-tuning: which one is better for your AI project?

RAG integrates external knowledge sources to improve performance by retrieving relevant documents during inference, making it ideal for tasks that require dynamic, up-to-date information. In contrast, Fine-tuning adapts pre-trained models to specific tasks through supervised learning, optimizing performance within static, well-defined domains. We analyze the trade-offs in terms of data requirements, deployment flexibility, cost, and use cases.