RAG vs Fine Tuning || War between 2 AI greats #RAG #finetuning #llm

In modern AI systems, two powerful approaches often get compared: RAG (Retrieval-Augmented Generation) and Fine-Tuning. RAG improves responses by retrieving relevant information from external sources like documents, databases, or vector stores at inference time. This allows models to use fresh, up-to-date knowledge without retraining the model. Fine-Tuning, on the other hand, modifies the model’s internal weights using additional training data. This helps the model learn new behaviors, domain expertise, or task-specific skills directly inside the model. Instead of choosing one over the othe…