Embedding

A numerical representation of text (or an image) as a vector with hundreds or thousands of dimensions, where the distance between vectors reflects semantic similarity. It’s the technical foundation that makes it possible to search “by meaning” rather than by exact words—and therefore underpins RAG, semantic search, and modern classification.

When should I use Embedding at a company?

Search systems for internal knowledge bases, ticket classification by topic, duplicate detection, and recommendations based on content similarity. Any use case previously handled with SQL and keywords can be improved with embeddings.

When is it NOT a good idea Embedding?

Using embeddings for problems where exact rules are mandatory (e.g., SKU matching, legal compliance). An embedding means “similar,” not “identical.”