Embeddings

One of the core backbones of modern AI is the concept of embeddings—a way to transform data (text, images, or audio) into a mathematical representation that can be mapped in a vector space. Unlike traditional computing, which sees words as just letters and characters, embeddings capture semantic meaning—how humans actually understand words and concepts. For example, while “hand” and “band” differ by only one letter, their meanings are very different; embeddings recognize this, placing “hand” closer to “foot” than to “band” in a vector space.

This approach enables AI to group similar concepts together, allowing for powerful applications like similarity searches, recommendation systems, and contextual understanding in chatbots. AI can now recognize relationships like “a bird’s nest is similar to a lion’s den” or “a house is similar to a home” even if they don’t share the same words. Embeddings are a key component in Retrieval-Augmented Generation (RAG) systems, which we’ll dive into next.