Fine-Tuning vs. RAG: When To Use Each for Production LLMs

This guide explores the differences between fine-tuning and Retrieval-Augmented Generation (RAG) in AI applications. RAG allows a model to access external information not included in its training data, making it a popular choice for teams needing frequently changing data. On the other hand, fine-tuning involves training an existing model on additional examples to teach it new behaviors, making it suitable for specific tasks like healthcare or finance. While fine-tuning was once common, RAG has become the preferred option for many applications.