AI Glossary
Embedding Model
An AI model that converts information into embeddings so that relationships between concepts can be understood mathematically.
Embedding Model
Overview
Computers do not naturally understand meaning the way humans do.
To help AI systems identify relationships between words, documents, images, and concepts, information must first be converted into a numerical form.
This is the role of an embedding model.
An embedding model creates Embeddings, which are numerical representations that capture similarities and relationships between pieces of information.
A helpful way to think about an embedding model is a translator. Instead of translating between languages, it translates human concepts into a mathematical format that machines can understand.
These embeddings are often stored in a Vector Database, allowing AI systems to retrieve information based on meaning rather than exact wording.
Embedding models play a critical role in semantic search, recommendation systems, AI assistants, and modern enterprise AI applications.
Why It Matters
Embedding models help AI systems understand relationships between concepts and information.
Real-World Example
A search engine may use an embedding model to understand that “car” and “automobile” refer to similar concepts.