Embedding

Plain-English explanation

Embeddings map content into vectors so similar items are near each other. They support semantic search, recommendations, clustering, and deduplication.

Why it matters

Embedding matters because it affects how AI systems are designed, evaluated, priced, or trusted. Knowing the term helps you ask better questions and avoid vague implementation decisions.

• Ask how it changes quality, cost, speed, or safety.
• Look for concrete examples in the workflow you are building.
• Document the tradeoff before choosing a tool or architecture.