Fine-Tuning vs RAG: Updating Model Weights vs Retrieving External Knowledge
Overview Fine-tuning and RAG (Retrieval-Augmented Generation) are two ways to make a large language model produce better, more relevant answers, but they intervene at different points in the pipeline. Fine-tuning permanently adjusts the model’s weights through additional training, while RAG leaves the model untouched and instead injects context by retrieving documents at query time. The choice matters because it determines how you update knowledge, control latency and cost, and trace where an answer came from. ...