# Why Would You Ever Fine-tune an LLM?

You can get pretty good results with few-shot prompt templates, why would you go to the trouble of spending expensive GPU hours to fine-tune an LLM with essentially the same process as few-shot prompt templates?

It basically comes down to data. How much do you have?

![](https://cdn.hashnode.com/res/hashnode/image/upload/v1722501501257/2ee8294e-a349-4637-b139-3ec7d1886b21.webp align="center")

If all the data you have available is what you can personally type yourself, you probably don't have enough data to do fine-tuning on your LLM. In this case, you will get the best results with few-shot prompt templates.

On the other hand, if you can come up with 1000s of example prompt/response pairings, then fine-tuning will allow you to "bake" all of that knowledge into your LLM so that all of it can be taken advantage of on every query. With few-shot and several-shot templates you can only pass a few of these examples to the LLM on each query, so if you have a wide vocabulary or lots of different types of conversation topics to cover, fine-tuning is the way to go.