Embeddings

Embeddings are a technique for turning words, sentences, images, or other data into lists of numbers (called vectors) that represent their meaning. The clever part is that things with similar meanings end up with similar numbers. For example, the embeddings for "dog" and "puppy" would be very close together numerically, while "dog" and "spacecraft" would be far apart.

Why is this useful? Because computers are great at comparing numbers but terrible at understanding meaning directly. By converting text into embeddings, you give the computer a way to measure how similar two pieces of text are. Is this customer question similar to a question in our FAQ? Are these two articles about the same topic? Embeddings make these comparisons fast and accurate.

The way it works is that an AI model processes the input and produces a long list of numbers -- typically hundreds or thousands of numbers per item. Each number captures some aspect of the meaning. You do not get to choose what each number represents; the model figures that out during training. But the result is a kind of "meaning map" where similar items cluster together.

Embeddings are a behind-the-scenes technology that powers many AI features. Search engines use them to find results that match the meaning of your query, not just the exact words. Recommendation systems use them to find similar products or content. And RAG systems use them to find the most relevant documents to answer your questions. You rarely interact with embeddings directly, but they are working in the background of most AI applications.