Embeddings

Embeddings are numerical representations of text, images, or other data that encode meaning in a way computers can compare. Rather than matching exact words, embeddings capture semantic similarity: a query about "vacation policy" can retrieve documents about "annual leave" or "time off" because their meanings map to similar positions in the embedding space. They are the technical foundation for semantic search, retrieval-augmented generation, recommendation systems, and content deduplication. The practical value is significant: systems can find relevant information even when the words don't match.

Embeddings are an information architecture problem as much as a technical one. They're only as useful as the content they represent — stale documents return stale answers, restricted documents can be retrieved by the wrong users if permissions aren't enforced, and poor content organization makes retrieval unreliable. Organizations that think of embeddings as a model capability rather than an information management challenge consistently underinvest in the governance layer and discover the gaps when their AI assistant confidently surfaces the wrong answer.