Embeddings are lists of numbers that capture the meaning of a piece of data, whether that’s a sentence, a product description or an image. The clever part is that distance has meaning: two embeddings that sit close together represent things that mean roughly the same, and two that sit far apart represent things that don’t.
Imagine plotting every word on a map where related ideas are neighbours. “Dog” and “puppy” sit a street apart, “dog” and “spreadsheet” are in different cities. That map is what embeddings give you, and an embedding model is what draws it. Once your data lives as embeddings in a vector database, you can find the closest matches to any query in milliseconds.
A real list is longer than a map suggests. A typical embedding has hundreds or even thousands of numbers, one per dimension of meaning, far more than the two directions you can draw on paper. You never read those numbers yourself. The system measures how near two of them sit, usually with cosine similarity, and returns the closest.
This is what makes modern semantic search work. Instead of matching exact keywords, the system compares meaning. But embeddings only know what their model was trained on: a model that never saw your internal product codes will treat them as gibberish, so very niche jargon can still need a keyword fallback alongside.
One more thing worth knowing. Embeddings are fixed once made, so if your meaning shifts, a rebranded product or a new house style, the old vectors keep pointing at the old meaning until you regenerate them. That is why a search index is something you refresh, not something you set and forget.
At TopDevs we use embeddings as the foundation for smart search and AI assistants, so a client’s customers can ask a question naturally and still land on the right answer.
