An embedding is a way of turning text into a list of numbers (a vector) that captures its meaning, so a model can measure how related two pieces of text are by how close their vectors sit. Words and passages about similar concepts land near each other, even when they share no exact words.
This is the machinery behind semantic search and the retrieval step in RAG: when an engine looks for sources to answer a question, it compares the question's embedding to the embeddings of candidate passages and keeps the closest matches.
The practical takeaway for content: relevance is now conceptual, not keyword-frequency. A page about "engine misfires" should also naturally cover spark plugs, rough idling, and check-engine lights, so its embedding spans the full topic and gets matched to more of the questions people actually ask. Covering a topic's real vocabulary also strengthens your entity association with it.