What an embedding is and why it finds things by meaning
Classic search finds identical words. If a customer types "return" and your document says "refund and send back", it won't find it. With embeddings, the system compares meaning: "return", "send back" and "I want to post the parcel back" end up close together.
Embeddings are the hidden engine behind many useful functions: searching company documents, RAG, grouping customer requests by topic, suggesting similar products.
An example in an SMB
A furniture e-commerce shop turns all its product descriptions into embeddings. When a customer searches for "small table for balcony", the site also shows the "folding outdoor side table", which a word-based search would not have returned.
The most common mistakes
- Thinking the embedding understands everything: very technical terms or internal abbreviations can be represented badly.
- Mixing embeddings created with different models: they cannot be compared with each other.
- Forgetting to regenerate them when the documents change.
- Using only search by meaning when the user is looking for an exact code: there, word-based search works better, and it is often worth combining the two.
Why it matters to you
When a supplier offers you smart search on your website or an assistant over your documents, there are almost always embeddings underneath. Knowing that they work by similarity of meaning helps you ask the right question: how are article codes and abbreviations handled, since by meaning they say nothing?
All the words of AI explained simply are in the glossary.
Frequently asked questions
Are embeddings expensive?
Usually much less than generating text, and they are calculated once per document. You can also create them with models that run locally.
Do I need a programmer?
For serious use, yes, although many chatbot tools already handle them internally without you seeing them.
Can an embedding be turned back into the original text?
Not directly, but it should not be treated as anonymous data: protect it like the documents it comes from.
Read next
What a vector database is and when you really need one
A vector database is a store built to hold embeddings and quickly find the ones closest to a question, that is, the texts most similar in meaning.
GlossaryWhat is RAG: AI that answers from your documents
RAG (Retrieval Augmented Generation) is a technique where, before answering, the AI searches your documents for the useful passages and uses them as the basis for its answer.
GlossaryWhat's an LLM? The model behind ChatGPT, Claude and Gemini
Almost every AI tool you are offered uses one. Often it is the same one, resold at different prices.
GlossaryWhat semantic search is and how it helps your business
Semantic search finds documents based on the meaning of your question, not just the exact words it contains. If you search for "how do I recover my password", it also finds the text that talks about "resetting your login credentials", because the concept is the same.
Want to understand what your business really needs, without the jargon? Let's talk.
Text and graphics created with the support of AI. Method and content are AIR's.