RAG vs. Fine-Tuning Models: What’s the Right Approach?

Retrieval-Augmented Generation (RAG) retrieves relevant documents to generate contextually accurate responses, ideal for dynamic environments like enterprise search and customer support. Fine-tuning involves training a model on specific datasets for specialized tasks, ensuring consistency and improved performance for targeted applications. Choosing between RAG and fine-tuning depends on the need for adaptability or task-specific expertise.