# Retrieval-Augmented Generation vs Fine-Tuning

> Retrieval-augmented generation gives a model relevant documents at query time, so it can use current or private information without changing the model. Fine-tuning further trains the model's weights on examples, which is better for teaching a consistent format, style or specialized skill than for adding facts that change.

- Canonical URL: https://protologue.com/compare/rag-vs-fine-tuning/

## Retrieval-Augmented Generation

Retrieval-augmented generation (RAG) supplies a language model with passages retrieved from an external corpus at query time, so its output is grounded in that information rather than only in its trained parameters.
Entry: https://protologue.com/t/retrieval-augmented-generation/

## Fine-Tuning

Fine-tuning is the further training of a pretrained model on a smaller, task- or domain-specific dataset so that its weights adapt to that task or domain.
Entry: https://protologue.com/t/fine-tuning/

## Differences

| | Retrieval-Augmented Generation | Fine-Tuning |
|---|---|---|
| What changes | The prompt (retrieved passages are inserted) | The model's weights |
| Updating knowledge | Re-index the documents; no retraining | Requires another training run |
| Best at | Grounding answers in specific sources | Consistent behavior, format and style |
| Main costs | A retrieval pipeline and longer prompts | Training data, compute and a separate model to maintain |

## Which to use

- Use RAG when answers must reflect documents that change or must be cited.
- Use fine-tuning when instructions and examples in the prompt cannot reliably produce the behavior you need. The two are often combined.

## Sources

- Lewis et al. (2020). Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks. https://arxiv.org/abs/2005.11401
- Howard and Ruder (2018). Universal Language Model Fine-tuning for Text Classification. https://arxiv.org/abs/1801.06146
- Hu et al. (2021). LoRA: Low-Rank Adaptation of Large Language Models. https://arxiv.org/abs/2106.09685

## Cite this page

Protologue. (2026). Retrieval-Augmented Generation vs Fine-Tuning. In Protologue: A Taxonomy of Prompting and LLM Techniques (v1.4.0). https://protologue.com/compare/rag-vs-fine-tuning/

License: CC BY 4.0 (https://creativecommons.org/licenses/by/4.0/)