Comparison
Fine-tuning vs Retrieval-Augmented Generation
Fine-tuning and Retrieval-Augmented Generation are both common AI/LLM terms but cover different ideas. Here is a quick side-by-side.
When you would reach for Fine-tuning
After you've exhausted prompting and retrieval, and you have a few hundred to thousands of clean labeled examples.
Fine-tuning Llama 3 on medical Q&A for a clinical assistant.
When you would reach for Retrieval-Augmented Generation
When the model needs information that is not baked into its weights — fresh, private, or domain-specific.
"Chat with your PDFs" — Notion, Glean, ChatGPT custom GPTs.
Frequently asked
What is the difference between Fine-tuning and Retrieval-Augmented Generation?
Fine-tuning: Fine-tuning continues training a pretrained model on a smaller, task-specific dataset, adjusting its weights to specialize behavior or knowledge. Retrieval-Augmented Generation: RAG retrieves relevant documents from a corpus at query time and includes them in the prompt, letting an LLM answer with up-to-date, source-cited, private information without retraining.
When should I use Fine-tuning vs Retrieval-Augmented Generation?
After you've exhausted prompting and retrieval, and you have a few hundred to thousands of clean labeled examples. When the model needs information that is not baked into its weights — fresh, private, or domain-specific.
Are Fine-tuning and Retrieval-Augmented Generation the same thing?
No. Fine-tuning is training; Retrieval-Augmented Generation is agents & tools. They are related but address different parts of the AI stack.