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

What are best practices for RAG vs Fine-Tuning

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Answer

When working with RAG vs Fine-Tuning, start with a clear goal, test on real data early, keep the approach as simple as possible at first, and follow established practices from the AI community rather than guessing.

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