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Retrieval Augmented Generation (RAG) › RAG vs Fine-Tuning

How does RAG vs Fine-Tuning work

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Answer

RAG is often faster to update since it just requires changing the external knowledge source, while fine tuning requires retraining the model itself whenever the underlying knowledge changes.
Real-world example A company chooses RAG over fine tuning so it can update its chatbot's knowledge simply by updating documents, without retraining the entire model.

Common follow-ups: What is Retrieval Augmented Generation (RAG), How is RAG vs Fine-Tuning evaluated in practice, What tools are commonly used for RAG vs Fine-Tuning

Retrieval Augmented Generation (RAG) topics: Introduction to Retrieval Augmented Generation Vector Databases for RAG Embedding Models for Retrieval
All 7 RAG vs Fine-Tuning questions