What’s up guys? Welcome back to the covers. In this video, we’re going to learn the differences between zero shot, few shot, and chain of thought prompting. These three are the main types of prompting, so let’s begin with the first one, zero shot prompting. It is a technique where a language model is able to generate responses to a prompt it has never been explicitly trained on. It achieves this by understanding the general context and structure of the prompt, allowing it to generate coherent and relevant responses. With zero shot prompting, there is no need to provide examples. We just need to set the equations or instructions and let the model answer them without providing examples.
Let’s take an example. Suppose we want to ask, ‘What is the color of the moon?’ We can simply ask this question without providing any examples. The model will generate a response based on its understanding of the prompt. For example, the color of the moon appears to be mostly gray or white.
Now, let’s talk about few shot prompting. Few shot prompting involves training the model on a limited number of examples related to a specific problem. This enhances its ability to generate accurate responses within that domain. To use few shot prompting, we provide examples or instructions to the model, specifying our expected output. For example, if we want to generate ad copy for our sneaker products, we can provide a few examples of the desired structure and ask the model to generate similar ad copy. The model will then generate ad copy for our sneaker products with the same structure as the examples.
When to use zero shot prompting or few shot prompting depends on your specific needs. If you want the model to generate complex templates or concepts, few shot prompting is recommended as it allows you to train the model to understand your desired output. On the other hand, if you want the model to generate new ideas freely, without being restricted to specific examples, zero shot prompting is recommended.
Finally, let’s discuss chain of thought prompting. Chain of thought refers to the ability of language models to maintain coherent and logical progressions in conversations by understanding and referencing prior context and information. With chain of thought prompting, you can have continuous conversations with the model. You can ask questions, and the model will provide answers based on the previous context. For example, you can ask the model to generate ideas for your e-commerce business, and based on its response, you can ask further questions related to the previous answers.
In conclusion, zero shot, few shot, and chain of thought prompting are three different techniques for interacting with language models like GPT. Zero shot prompting allows the model to generate responses without providing examples, few shot prompting involves training the model on a limited number of examples, and chain of thought prompting enables continuous conversations with the model. Understanding these techniques can help you effectively utilize language models for various tasks.
