Fine-tuning
The method of fine-tuning involves taking datasets relevant to your inputs and outputs and building out a comprehensive model. This is something not many people are doing.
Fine-tuning is often overlooked in the AI space and is not used as much as Prompt Engineering. Both have advantages and weaknesses and exploring and understanding both will give you a higher level of understanding of AI and the possibilities when using it. Fine-tuning will hands down give you the best models possible and the best outputs possible so what is fine-tuning?

It is all about the data!

Data is incredibly powerful and is where AI shines, the more you feed it, the better outputs you are going to get. Often it is the case that AI engineers do not have the patience, skill or ability to put together a dataset for the problems that they want to solve. Putting together a dataset though will give you incredible results.
Let's take an example of building a product description generator. A way that you can do it is literally to write in the prompt the following;
1
Write me an exciting product description for the following product
2
3
Product Title:
4
Product Desscription:
5
###
6
Copied!
That is about as basic as it gets, you tell the AI what you want it to do and you give it some information so that it can do it. You can then set the Temperature, Top P and other AI settings and then when you hit generate on that and have filled in the inputs (which will fix in the text on the 3rd and 4th line, it will give you the output on line 6. It works, it gives you a product description but it lacks depth, quality and your voice or tone. That sucks.
Whilst Content Villain started out using prompt engineering entirely. We are in the process of building out datasets and fine-tuning every single one of our models as the fine-tuning process is superior. Some of our custom model clients have used their own datasets in fine-tuned models and are getting awesome results.
What if we had 10,000 examples of input pairings and desired outputs. These have been vetted, they have been scored for quality and we only use the top examples in the dataset. We feed in that as a fine-tuning model, the AI devours that information and understands from those 10,000 examples exactly the type of content, tone of voice, style and everything else that we want to get out. That is fine-tuning. That is how to build a superior model and create something which a lot of other tools don't have because they won't build out that dataset.
Last modified 28d ago
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