Embeddings

Embeddings are numerical representations of information that let you compare relationships within a vector space. In marketing and AI systems, they help retrieve documents by semantic similarity, group comments, or find related content beyond a literal word match.

How do embeddings work?

A model turns texts or other formats into vectors. The application compares those vectors using a distance or similarity measure. The results depend on the model, the content being represented, and how you search.

How is this applied in marketing?

An internal search can connect a question about changing a purchase with a document titled return policy. Then you verify that the retrieved snippet actually answers the user’s need.

Frequently asked questions about embeddings

Does an embedding store the original document?

It is a numerical representation. To display or verify a source, you need to keep access to the corresponding content.

Are they only used for text?

No. Some models represent images, audio, or other formats. Check the capabilities of the model you choose.

Is similarity proof of truth?

No. It indicates closeness in the representation, not factual accuracy. Cross-check the results against the source.

Can they be used to group comments?

They can support thematic groupings. Review samples to confirm the groups make sense for the business.

How do you evaluate a semantic search?

Test real queries and measure whether it retrieves relevant documents. Include questions with no answer and texts with similar meanings.

Embeddings: limits you should know

Semantic closeness does not prove that two statements are true or equivalent. A retrieval system must evaluate relevance, dates, and permissions before using the snippets it finds.

To broaden the context, compare this concept with RAG, LLM and Grounding.

Reference source: docs.cloud.google.com. Glossary by Digital Agency AMD.