Intro to Generative AI, Training Data, Encoding, Decoding, Probability Distribution, Input, ChatGPT

Thanks for watching, “Intro to Generative AI, Training Data, Encoding and Decoding, Probability Distribution, User Input”, this is based on Codecademy’s Intro to ChatGPT, we are in the Introduction to Generative AI section. We learn about the following. Introduction:

Generative AI is a powerful tool that allows machines to generate new content that resembles human-created work. It is a technology that is rapidly evolving, and in this lesson, we will explore the basics of generative AI and how it is used in ChatGPT, one of the most popular tools in the field. By the end of this video, you will have a better understanding of how generative AI works, how it can be used, and the key steps involved in the process.

Collect Training Data:

The first step in using generative AI is to collect training data. This can be done by gathering a large amount of data in a specific domain, such as text, images, or music. The training data is then used to teach the machine how to create new content that resembles the original data.

Encode Training Data:

Once the training data has been collected, it must be encoded into a format that the machine can understand. This is done using various encoding techniques, such as natural language processing, image recognition, or audio processing.

Train and Create a Probability Distribution:

Once the data has been encoded, the machine must be trained to understand the patterns and relationships in the data. This is done by creating a probability distribution, which is a mathematical model that represents the likelihood of different outcomes based on the training data.

Extra Learning & Filtering:

In some cases, additional learning and filtering may be required to improve the accuracy of the generative AI model. This can be done using techniques such as reinforcement learning or unsupervised learning.

Collect and Encode User Input:

To generate new content, the machine must first receive input from the user. This input is collected and encoded using the same techniques used to encode the training data.

Generate Content:

Once the user input has been encoded, the machine can generate new content based on the probability distribution created during the training phase. This content can be in the form of text, images, or music.

Decode and Output Generated Content:

Finally, the generated content is decoded and output to the user. In the case of ChatGPT, this is in the form of human-like text responses to engage users in a reasonable conversation.


To summarize, the process of using generative AI involves collecting and encoding training data, training the machine to understand patterns and relationships in the data, creating a probability distribution, and collecting and encoding user input. The machine then generates new content based on the probability distribution and outputs it to the user. With this knowledge, you are now equipped to explore generative AI tools with a better understanding of how they work.

In conclusion, generative AI is a powerful tool that has the potential to revolutionize many industries. By understanding the key steps involved in the process, you can start exploring the possibilities of this technology and how it can be used to create new content in various domains. Whether you are interested in text, images, or music, there are many applications for generative AI, and the possibilities are endless.