Prompting vs RAG vs Fine-Tuning: Which Do You Need?
When a general AI model doesn't quite do what you want, there are three main ways to improve it. Start with the simplest.
1. Better Prompting
Give clearer instructions, examples and context.
- Use when: the model is capable but you haven't told it enough.
- Pros: fast, free, easy to change.
- Cons: limited by context length and consistency.
2. RAG (Retrieval-Augmented Generation)
The system looks up relevant documents and gives them to the model alongside your question.
- Use when: it needs your up-to-date or private knowledge: policies, manuals, product data.
- Pros: answers can cite sources, easy to update, no retraining.
- Cons: depends on document quality and good retrieval.
3. Fine-Tuning
You train the model further on examples to change its behaviour, style or format.
- Use when: you need consistent tone, structure or a specialised task and have lots of good examples.
- Pros: consistent outputs, shorter prompts, can use smaller models.
- Cons: needs quality data, skill and cost. Knowledge becomes stale, and it can be hard to update.
Quick Decision Guide
| Problem | Try first |
|---|---|
| Output is vague or wrong format | Better prompt with examples |
| Needs facts from our documents | RAG |
| Needs a very specific consistent style or classification | Fine-tuning |
| Needs both knowledge and style | RAG + fine-tuning |
Common Mistakes
- Fine-tuning to teach facts. RAG is usually better for knowledge that changes.
- Skipping evaluation. Build a test set of real questions and check results.
- Ignoring data quality.
- Over-engineering before trying a good prompt.
Takeaway
Prompt first, add RAG for knowledge, and fine-tune only when you have a clear behaviour problem and the data to solve it.
