The process of structuring and refining your content, metadata, and structured data so that the vector embeddings—numerical representations of text used by AI retrieval systems—accurately capture the intended semantics and relationships of your key entities and topics.

Goal: Ensure that the numerical vector generated from your text by an embedding model accurately encodes the meaning you intend.

• Focus: How AI models encode and cluster your content.

• Tactics:

  • Rewriting content to reduce ambiguity.

  • Structuring metadata so key entities are consistently represented.

  • Adjusting context so embeddings preserve intended relationships.

• Output: Higher-quality, more coherent embeddings that yield better retrieval, clustering, and AI citations.

• Analogy: Training your content to “pose” correctly so the camera (the embedding model) captures exactly what you want.