The importance of embeddings in generative AI - OpenAI API Tutorial

- [Instructor] Generative AI and AI powered tools are transforming the world. But what powers this innovation? And how do platforms like Spotify, Netflix, and YouTube provide personalized experiences to users? Part of it is something called embeddings, and embeddings are at the core of many AI tools, enabling them to understand and translate data into meaningful experiences. Grasping this concept is essential for anyone learning to build and work with AI. So first, let's get into what embeddings are, specifically text embeddings, which is the focus of this course. An embedding is a vector representation of text, be it a word, sentence, or document that allows AI models to process language in a way that captures the semantic meaning of words and phrases, and the relationships between other words and phrases. AI is really good at understanding numbers. This numerical form is what allows models to understand text in a human-like way, but through mathematical relationships versus intuitive understanding. These numbers are not random. They're learned from data. AI models are trained using massive amounts of text. They can figure out patterns and relationships between words, and they encode this information into the embeddings. Imagine vector embeddings as points on a graph. The vectors are carefully calculated so that vectors representing words with similar meanings get plotted closer together, while unrelated words are farther apart. Behind the scenes, special algorithms are at work measuring the distance between vectors to assess the closeness or similarity between these points. This measurement helps determine how closely related to words or concepts are. That's how AIs are able to understand language and how words relate to each other. For example, a support ChatBot designed for a tech conference might get questions like, what time is the keynote? Or who's presenting in the AI panel? Through embeddings, the ChatBot can understand that these inquiries are about session schedules or speaker details. Most importantly, embeddings help AI figure out the context, like whether the word bark refers to a tree or a dog based on the conversation. This makes AI seem more human, able to chat with us, suggest songs or recommend movies and shows that we'll enjoy. As you've learned, embeddings convert text into numerical vectors that AI models can easily work with helping interactions with them feel more human. With this knowledge, you're ready to learn more about the technical details of embeddings, and how they are created and used in AI powered apps.