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.
Definitions
- Retrieval-Augmented Generation PTL-0055
- 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.
- Fine-Tuning PTL-0105
- 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.
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.
- Howard and Ruder (2018). Universal Language Model Fine-tuning for Text Classification.
- Hu et al. (2021). LoRA: Low-Rank Adaptation of Large Language Models.
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/