RAG vs. Fine Tuning

Retrieval augmented generation (RAG) and fine-tuning are two techniques for enhancing large language models. RAG retrieves external, up-to-date information to augment responses, making it effective for dynamic data sources and mitigating model hallucinations. Fine-tuning adapts a model to a specific domain or style by incorporating labeled and targeted data into the model's weights, providing more specialized and consistent outputs. Both techniques have their strengths and weaknesses, and the choice between them or a combination depends on specific use cases, data requirements, and desired model behavior.