Embeddings are how AI models represent meaning as numbers. Every word, sentence, or concept gets converted into a long list of numbers - a vector - positioned in a high-dimensional mathematical space. The clever part is that similar meanings end up close together in this space. "King" and "queen" are near each other. "Cat" and "kitten" are neighbours. The relationship between "Paris" and "France" mirrors that between "Tokyo" and "Japan." These aren't hand-coded rules - they emerge naturally from training on vast amounts of text. Embeddings are useful far beyond language models. They power search systems that find results based on meaning rather than exact keyword matches. They enable recommendation engines that understand what's similar to things you've liked before. They're how databases can store and retrieve information based on conceptual similarity rather than rigid categories. For business applications, embeddings are one of the most immediately practical AI concepts. If you're building a search feature, a recommendation system, or any tool that needs to understand similarity between pieces of content, you'll likely be working with embeddings - even if the technical details are handled by a library or API.