Definition
Embeddings are lists of numbers that represent the meaning of text, so software can find passages with similar meaning even when they use different words.
Embeddings explained
An embedding model converts a piece of text into a vector, a long list of numbers. Texts with similar meanings end up with similar vectors. “How do I reset my password?” and “I can't log in to my account” share few words but land close together, which is what makes semantic search possible.
Embeddings are usually stored in a vector database, which can quickly find the stored passages closest to a new query. This is the retrieval step in most RAG systems: the question is embedded, the nearest passages are found, and they are passed to a language model to write the answer.
Search engines and AI search systems use similar techniques to match queries with passages by meaning rather than exact keywords. That is one reason why writing clear, self-contained sections, each focused on one question, helps content get matched to the many ways people phrase the same need.
Example
Your internal knowledge tool stores embeddings of every policy document. When an employee asks “can I work from another country for a month?”, it finds the remote-work policy section even though that section never uses the phrase “another country”.
Why it matters
Embeddings power semantic search and RAG, and they explain why meaning and clarity now matter more than exact keyword matching.
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Related terms
- RAG (Retrieval-Augmented Generation)RAG (retrieval-augmented generation) is when an AI system first retrieves relevant documents, then has a language model write its answer from them.
- LLM (Large Language Model)An LLM (large language model) is an AI model trained on huge amounts of text to generate language, such as the models behind ChatGPT, Claude and Gemini.
- Query fan-outQuery fan-out is a technique where an AI search system splits one question into several related searches, then combines what it finds into a single answer.
- Context windowA context window is the most text, measured in tokens, that a language model can consider at once, including instructions, documents and its reply.