Fine-tuning adjusts a pre-trained language model on specific data to improve performance on targeted tasks. Retrieval-Augmented Generation (RAG) combines a language model with an external knowledge base, retrieving relevant documents to inform responses dynamically.
Fine-tuning retrains model weights using labeled examples, embedding domain-specific knowledge directly into the model. RAG, instead, queries an external database or corpus at runtime, feeding retrieved data to the model to generate context-aware answers without altering the model itself.
Fine-tuning offers tailored outputs but requires significant compute and time, with updates needed for new data. RAG provides up-to-date, scalable knowledge access with lower retraining costs, faster iteration, and reduced latency for applications needing real-time information integration.