Fine-Tuning vs Prompting vs RAG
There are three main ways to make a general AI model behave the way your business needs. Each suits different problems, and picking the wrong one wastes time and money.
This article compares them in plain terms.
The Three Options
- Prompting: instruct the model well each time — cheapest and quickest to try.
- RAG: feed the model your documents at answer time — best for current, factual answers.
- Fine-tuning: retrain the model on your examples — best for a consistent style or skill.
How to Choose
Start with prompting. If answers need your specific facts, add RAG. Only fine-tune when you need a consistent behaviour that prompting cannot achieve, as it is the most costly and least flexible to update.
| Method | Best for | Cost |
|---|---|---|
| Prompting | Quick tasks | Low |
| RAG | Factual answers | Medium |
| Fine-tuning | Consistent style | High |
Frequently Asked Questions
Can we combine these methods?
Yes, and many systems do — for example, a fine-tuned model used with RAG — though each layer adds cost and complexity.
If you need a hand with any of this, your Progressive Robot delivery team is ready to help. Raise a ticket from the Support area of your client portal or speak to your account manager and we will guide you through the next steps.