LLM architecture guide
RAG vs fine-tuning: which approach should you use?
RAG supplies current or private information to a model at request time. Fine-tuning changes model behavior using training examples. They solve different problems and can be used together.
Direct answer
What you should know
Use RAG when answers must reflect changing, private or citable knowledge. Consider fine-tuning when the goal is consistent behavior, format or domain task performance that cannot be achieved reliably through prompts alone.
Choose RAG when
- Knowledge changes frequently.
- Sources must remain auditable or citable.
- Access depends on the user or document permissions.
- The organization already has useful documents or records.
Consider fine-tuning when
- The desired output format is highly repetitive.
- Prompt examples are insufficient for stable behavior.
- A specialized classification or transformation task has quality training data.
- The expected benefit justifies training and evaluation cost.
Evaluate before committing
Compare a strong prompt baseline, retrieval, tool use and fine-tuning against the same representative evaluation set. The simplest approach that meets the quality, privacy, latency and cost target is usually preferable.
Side-by-side comparison
| Dimension | RAG | Fine-tuning |
|---|---|---|
| Primary purpose | Ground responses in retrieved information. | Adapt model behavior with examples. |
| Knowledge updates | Update the source index without retraining the model. | Requires new training data and another tuning run. |
| Citations | Can return source links or passages. | Does not inherently provide citations. |
| Best for | Current documents, policies, product data and private knowledge. | Style, format, classification or repeated domain behavior. |
| Main risks | Poor retrieval, access leakage or irrelevant context. | Overfitting, weak training data or unintended behavior change. |
| Can combine? | Yes. Retrieval can ground a fine-tuned model. | Yes. Fine-tuning can improve behavior around retrieved context. |
Frequently asked questions
It can influence model behavior, but it is usually not the best mechanism for frequently changing facts. Retrieval is easier to update and cite.
Yes. Retrieval can use keyword, database, API or hybrid search. Vector search is one option rather than a requirement.
Yes. A tuned model can receive retrieved context when both behavior adaptation and current evidence are needed.