RAG (Retrieval-Augmented Generation) is great for tasks that need real-time access to external, dynamic knowledge without retraining the whole model.
Fine-Tuning, on the other hand, is perfect for improving an LLM’s performance on specific tasks or domains by training it on a tailored dataset.
So, if your LLM needs to stay current with new information or handle a variety of topics, RAG is the way to go. If you need deep expertise in a specialized area, Fine-Tuning would be more effective.
Thanks for sharing your observation.
IMO it’s useful to rely on RAG even if the LLM is fine-tuned on the target domain. RAG provides sufficient context which the user might fail to include in the original prompt.
RAG (Retrieval-Augmented Generation): Enhances responses by retrieving external knowledge from a vector database. It’s cost-effective, requires no model retraining, and quickly updates information. Ideal for real-time data needs (e.g., news or FAQs).
Fine-Tuning: Adjusts the model’s internal weights using labeled datasets, enabling it to specialize in specific tasks or styles. It is expensive and time-consuming but offers tailored responses and consistent performance.
Choose RAG for real-time information and cost efficiency. Choose Fine-Tuning for domain expertise and stylistic consistency. Combine both for optimal results.