There are a number of different techniques that can be used to customize or fine-tune a large language model (LLM).

One popular technique is called TRANSFER LEARNING. With transfer learning, a model that has been trained on a large dataset of text is used as a starting point for training a BASE model for a SPECIFIC task. This is a good way to improve the performance of the model on a new task, as the model has already learnt some of the general concepts of language from the large dataset.

Here is a quick analogy boss for you- Let’s say you want to build a robot that can play basketball. You could start from scratch and try to teach the robot everything it needs to know about basketball, like how to dribble, shoot, and score. But that would take a lot of time and effort.

A better way to do it would be to use TRANSFER LEARNING. This means starting with a robot that already knows a lot about something else, like how to move around and interact with objects. Then, you can teach the robot the specific things it needs to know about basketball, like how to dribble, shoot, and score.

This technique is SO MUCH faster. 🙂

Other techniques that can be used to customize or fine-tune a LLM are PROMPT DESIGN and FINE-TUNING.