Embedding

An embedding is a high-dimensional vector — typically 768 to 3,072 numbers — that represents the semantic meaning of a piece of text, produced by an embedding model.

Embeddings are the bridge between human language and the math that semantic search and AI systems run on. Two pieces of text with similar meaning produce embeddings that are close in vector space (small cosine distance); unrelated pieces produce distant vectors. This property lets systems find "similar" content without ever comparing words directly.

In marketing tools, embeddings are used for retrieval (finding relevant past content), deduplication (detecting when a new draft is too similar to an existing post), and clustering (grouping posts into themes automatically). Different embedding models produce different vectors; switching models requires re-embedding everything, which is why production systems pick a model carefully up front.

Embedding quality determines how well a tool understands your content. Cheap or outdated embeddings produce noisy retrieval; current high-quality embeddings produce retrieval that actually surfaces the right past posts.