Side by side
RAG & Fine-tuning
RAG supplies retrieved information for generation; fine-tuning changes trainable model parameters. A system can use both.
This is not an either-or choice: a fine-tuned model may still use retrieval. The original RAG research also includes training.
Swap columns ⇄| Dimension | RAG | Fine-tuning |
|---|---|---|
| Purpose | Bring external evidence into an answer. | Adapt an existing model through further training. [Lewis et al.][Hugging Face] |
| Role in the system | A retrieval-and-generation arrangement. | A model adaptation process. [Lewis et al.][Hugging Face] |
| Inputs and outputs | A query and external collection inform a generated response. | Training examples and an existing model produce adapted parameters. [Lewis et al.][Hugging Face] |
| Persistence | The external collection can change separately from model weights. | The adapted parameters remain available after training. [Lewis et al.][Hugging Face] |
| Example | Retrieve the current product manual before answering. | Train on support examples to improve response behavior. [Lewis et al.][Hugging Face] |
| Boundary caveats | Retrieved material and the generated answer still need checking. | Training does not automatically provide current facts from an external collection. [Lewis et al.][Hugging Face] |
Evidence for this distinction
- Fine-tuning ↗ (opens in new tab)Hugging Face · Publication date unknown
Relevant section: Fine-tuning; Tokenization - Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks ↗ (opens in new tab)Lewis et al. · Publication date unknown
Relevant section: Abstract
Last editorial review: 2026-09-13.