Side by side
Fine-tuning & RAG
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 | Fine-tuning | RAG |
|---|---|---|
| Purpose | Adapt an existing model through further training. | Bring external evidence into an answer. [Lewis et al.][Hugging Face] |
| Role in the system | A model adaptation process. | A retrieval-and-generation arrangement. [Lewis et al.][Hugging Face] |
| Inputs and outputs | Training examples and an existing model produce adapted parameters. | A query and external collection inform a generated response. [Lewis et al.][Hugging Face] |
| Persistence | The adapted parameters remain available after training. | The external collection can change separately from model weights. [Lewis et al.][Hugging Face] |
| Example | Train on support examples to improve response behavior. | Retrieve the current product manual before answering. [Lewis et al.][Hugging Face] |
| Boundary caveats | Training does not automatically provide current facts from an external collection. | Retrieved material and the generated answer still need checking. [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.