All 7 RAG vs Fine-Tuning questions
More in RAG vs Fine-Tuning
What is RAG vs Fine-Tuning Why is RAG vs Fine-Tuning important in Retrieval Augmented Generation (RAG) What are the key parts or types of RAG vs Fine-Tuning What are common mistakes to avoid with RAG vs Fine-Tuning What is a real world example of RAG vs Fine-Tuning What are best practices for RAG vs Fine-Tuning
Advertisement
Back to results for “Embedding Models for Retrieval”
Retrieval Augmented Generation (RAG) › RAG vs Fine-Tuning

How does RAG vs Fine-Tuning work

Beginner

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