# Fine-Tuning

> Fine-tuning is further training a base AI model on your own domain data so it performs better on your specific tasks, tone, and terminology.

_Source: https://plenaura.com/glossary/fine-tuning · Last updated: 2026-06-03 · Plenaura_

## Key points

- Changes how the model behaves; RAG changes what it knows. They're often combined.
- A small model fine-tuned for your domain can outperform a large general one.
- Builds a proprietary asset competitors can't easily replicate.

## FAQ

### Do we need fine-tuning, or is RAG enough?

If your need is current, citable knowledge, RAG is usually enough. If you need consistent domain behavior, format, or specialized performance, fine-tuning helps, and the two work well together.