RAG vs. Fine-Tuning: Key Differences and When to Use Each Approach

When integrating new data into AI systems, developers can choose between Retrieval-Augmented Generation (RAG) and fine-tuning, each with distinct trade-offs. RAG retrieves relevant documents at query time without altering the model, making it well-suited for frequently changing or large datasets where transparency and rapid updates are priorities. Fine-tuning adjusts the model's internal weights using a training dataset, offering lower latency and greater behavioral control, but at higher cost and with the risk of overwriting existing knowledge. A hybrid approach — fine-tuning on a stable base dataset while using RAG for newer information — is considered the most flexible option. The right choice ultimately depends on factors such as data stability, dataset size, latency requirements, and available resources.