Embeddings for Beginners

What's really cool is that these embeddings capture semantic relationships. This means they don't just know that "dog" and "canine" are related because they appear together often, but they understand deeper connections between words and concepts. This ability to capture meaning and context is what makes embeddings so powerful for various AI applications, especially in text search and natural language processing.)

One of the most exciting applications of embeddings in recent times is their use in Retrieval Augmented Generation (RAG) for generative AI. RAG combines the power of large language models with a knowledge base, using embeddings to retrieve relevant information. This approach allows you to create AI systems that can generate responses based on specific data sets, making them more accurate and contextually relevant.)