RAG vs Fine-Tuning Explained: What They Actually Do and When to Use Each

Retrieval-Augmented Generation (RAG) and fine-tuning are two distinct techniques for enhancing large language models that solve different problems. RAG provides external knowledge to a model at inference time by retrieving relevant information from a database and adding it to the prompt, making it ideal for answering questions about specific or private data. In contrast, fine-tuning modifies the model itself by continuing its training on a specialized dataset to alter its behavior, style, or output format. The techniques are not mutually exclusive; RAG is best for knowledge injection while fine-tuning is for behavioral adaptation, and they can be used together for optimal results.