What are embeddings?
Embeddings are lists of numbers that represent the meaning of a piece of text, image or other content, so that items with similar meanings end up close together and software can find related material even when the wording differs.
Keyword search finds documents that contain the exact words you typed. Embedding-based search finds documents that mean something similar. A technician searching for 'unit blowing cold air' can find a note titled 'heat pump stuck in defrost' because the two sit near each other in the model's map of meaning, even though they share almost no words.
Embeddings are the quiet machinery behind most AI knowledge assistants. Your documents are split into chunks, each chunk is converted into an embedding, and those are stored in an index. When someone asks a question, the question is converted the same way and the closest chunks are retrieved and passed to a language model to write the answer.
You will rarely handle embeddings directly, but a few decisions affect results. How documents are chunked matters: split a contract mid-clause and retrieval suffers. Embeddings must be refreshed when documents change, or the assistant will retrieve stale text. And embeddings made from confidential files deserve the same protection as the files themselves.
Example
For example, a CPA firm could embed its library of past client memos and research notes. During tax season, a senior accountant searching for guidance on a home office deduction question would find a relevant memo even if it was filed under a different heading. The accountant still checks current IRS guidance before advising a client.