Embeddings

Lists of numbers that represent the meaning of a piece of text, image, audio, or code, allowing a computer to compare items by how similar their meanings are.

An embedding model reads a sentence like "How do I reset my password?" and converts it into a list of numbers, typically hundreds or thousands of them. A different sentence with similar meaning, like "I forgot my login credentials," gets a very similar list. Comparing these number lists mathematically lets software find related content without exact keyword matches. This is the foundation behind semantic search, recommendation engines, and content clustering.

Builder example

Every time your app finds "related articles" or searches company documents by meaning, embeddings are doing the work underneath. Choose an embedding model trained on web text and then use it to search legal contracts, and the similarity scores will be unreliable: the model has weak intuition for legal language. Picking an embedding model that understands your domain makes the entire search pipeline more accurate.

A user searches for how to cancel their account. The embedding puts 'cancel my account' near 'set up my account' because both are about account actions.

Test embeddings with your real queries and edge cases. Combine embedding search with keyword filters to catch these near-misses.

Common confusion: Embeddings measure pattern similarity, not factual correctness. Two sentences can be "close" in embedding space while saying opposite things, because the model sees them as topically related.