Definition
Embeddings are a way of converting text — words, sentences, or entire documents — into lists of numbers (vectors) that capture the meaning of that text. Two pieces of text that mean similar things will have similar numerical representations, even if they use completely different words. For example, “the car broke down” and “the vehicle stopped working” would produce embeddings that are close together numerically, while “the stock market rallied” would produce a very different set of numbers. This mathematical representation of meaning allows computers to compare, search, and organise text by concept rather than by exact wording.
Why It Matters
Traditional keyword search only finds exact matches. If your knowledge base uses the word “revenue” but a user searches for “income”, a keyword search misses the connection. Embeddings solve this by working at the level of meaning rather than words. This unlocks semantic search (finding results by meaning), content clustering (grouping similar documents together), recommendation systems (suggesting related items), and duplicate detection (finding near-identical content even when phrased differently). For businesses with large volumes of unstructured content — help articles, product descriptions, internal documentation — embeddings make that content genuinely searchable and useful in ways keyword matching cannot.
Example
A software company has a knowledge base of eight hundred support articles. Customers frequently search for help but use different terminology from the article titles and headings. Keyword search returns no results for many valid queries. The company generates embeddings for every article and replaces keyword search with semantic search. Now when a customer types “my export is stuck”, the system finds the article titled “Troubleshooting file download failures” because the embeddings recognise the conceptual similarity. Support ticket volume drops because customers find answers they previously could not.