Embeddings

What are embeddings?

Embeddings are numerical representations of data such as text, images, or audio. An embedding model converts each item into a list of numbers called a vector. Items with related meaning tend to sit closer together in that vector space.

For example, “hand” and “band” differ by only one letter, but their meanings are different. A useful text embedding should place “hand” closer to “foot” than to “band” because it represents meaning rather than spelling.

How vector similarity works

Applications compare vectors using a distance or similarity calculation. The result estimates how closely two items are related according to the embedding model.

This supports tasks such as:

• Semantic search that matches meaning rather than exact words
• Recommendations based on similar products, documents, or users
• Grouping related items into clusters
• Finding duplicate or near-duplicate content
• Retrieving context for AI applications

How embeddings support RAG

In a Retrieval-Augmented Generation system, document chunks are converted into embeddings and stored with their original text. The user's question is also converted into an embedding. The system compares that query vector with the stored vectors and returns the closest document chunks.

Those chunks become context for the language model. The embedding model retrieves the material; the language model uses it to generate an answer.

What embeddings do not prove

Vector similarity is not the same as factual accuracy. Two passages can be semantically related while disagreeing with each other. A close match can also be incomplete or outdated.

Good retrieval therefore depends on the embedding model, source data, metadata filters, chunking strategy, and an evaluation set that tests real questions.

Embeddings let software work with relationships that exact keyword matching can miss. They are a retrieval tool, not a source of truth.