AI applications go beyond conversational chatbots and general use cases. Companies want their AI models to have industry insight, use internal data, and produce a good response. To achieve this goal, companies have two primary options- retrieval-augmented generation (RAG) and fine-tuning.
The debate between RAG vs fine-tuning arises since each method contributes to the improvement of AI performance differently. While RAG enables AI models to receive updated information from external sources, fine-tuning trains the model to respond appropriately.
In this post, we discuss how both techniques operate in practice. First things first. Let’s look at what makes them different from each other.
Understanding Core Differences
What differentiates fine-tuning from RAG is the method in which the AI model uses the data.
