What Are Embeddings?

An embedding model converts input into a vector, a fixed-length array of numbers positioned in a high-dimensional space. Closeness in that space corresponds to closeness in meaning, so "how much does it cost" and "what are your prices" land near each other even though they share almost no words. Similarity is usually measured with cosine similarity or a related distance metric.

Embeddings are the foundation of semantic search, recommendation systems, clustering and deduplication. In a retrieval pipeline, documents are split into chunks, each chunk is embedded once and stored, and an incoming question is embedded at query time so the closest chunks can be pulled back as context for a language model.

Clerkzo embeds the pages it crawls from a customer's website so that visitor questions can be matched to the right content by meaning rather than keyword. That is why a visitor can phrase a question in their own words and still get an answer drawn from the correct page.