Almost every AI system that searches, recommends or retrieves information uses embeddings somewhere, and they are rarely explained to the people paying for them. Having embeddings explained in plain terms makes it much easier to judge what a proposed system will and will not do.
The short version: an embedding turns a piece of text into a list of numbers that captures its meaning, so that texts about similar things end up with similar numbers.
What that makes possible
Once text has been turned into these numbers, a system can find the passages closest in meaning to a question, even when they share no words. A search for “staff leaving” can find a document about “employee turnover”, which keyword search would miss entirely.
That single capability sits underneath semantic search, document retrieval for chat assistants, duplicate detection, clustering customer feedback into themes, and matching support tickets to past solutions.
Where embeddings work well
- Finding relevant passages in a large body of text written in ordinary language.
- Grouping similar items, such as feedback, tickets or product descriptions.
- Spotting near-duplicates that are worded differently.
- Recommending related content based on meaning rather than tags.
In each case the task is about similarity of meaning, which is exactly what embeddings capture.
Embeddings are very good at finding things that mean roughly the same. They are poor at anything that depends on an exact value, and most disappointments come from forgetting that.
Lena Fischer, Solutions Architect, Engineered With AI
Where they quietly fail
Exact matters are a weakness. Part numbers, invoice references, names and specific figures carry little meaning, so a search for a particular order number may return orders that are merely similar. Anything that depends on precise identifiers needs ordinary keyword or database search alongside.
Negation and small qualifiers are another. “Covered by the policy” and “not covered by the policy” can sit close together, because they are about the same subject. The system finds the right topic and the wrong answer.
Most real systems combine both
The practical design usually runs keyword and semantic search together and merges the results. That catches exact references and meaning-based matches, and it is noticeably more reliable than either alone.
The choices that matter
How documents are split into chunks affects results more than which embedding model is used. Chunks that are too large blur several topics together; chunks that are too small lose the context that makes them meaningful.
The embedding model should suit your language and domain, and switching models later means reprocessing every document, so the choice deserves testing on your own content before anything is built at scale.
Questions to ask a supplier
How are documents chunked, and was that tested? Is keyword search used alongside? How are results evaluated? What happens when a document is updated or deleted? Clear answers indicate a system that has been thought through.
The last question matters for accuracy and compliance alike. A retrieval index that still returns a withdrawn policy is a real risk, which ties into keeping personal data out of AI pipelines.
Judge it on your own questions
Collect fifty real questions your staff or customers ask, with the documents that should answer them, and test any proposed system against that list. Retrieval quality is easy to demonstrate on chosen examples and much harder on real ones.
If the right document appears in the top few results for most questions, the foundation is sound. If it does not, no amount of work on the model that writes the answer will fix it.
Costs are usually modest
Generating embeddings is cheap relative to running a large language model, and storing them is inexpensive at most business volumes. The meaningful costs sit in preparing documents, keeping the index current and evaluating quality, which are engineering hours rather than usage fees.
Multilingual content needs checking
Some embedding models place a question in one language close to the matching passage in another, which is useful for international teams. Others handle only one language well. If your documents or customers span several languages, confirm how the chosen model behaves before building on it.
Wondering if semantic search fits your data?
We will test it against your own documents and show you what it finds and what it misses.
