Semantic Search
Semantic search finds texts that match the meaning of a query, even if they use completely different words. Instead of comparing letter sequences, it compares the meaning of the search query and the document.
A classic search engine works with words. It searches its database for exactly the character strings that were typed in. Anyone who enters “car” won’t find a page that only talks about “automobile” or “vehicle”. Semantic search proceeds differently: it tries to grasp what is meant, not what is written. This is why it delivers matching results even when the query and the text don’t share a single word. The term “semantic” comes from linguistics and simply means “pertaining to meaning”.
Why keywords are often not enough
People formulate the same thing in many different ways. A customer writes “device won’t charge anymore”, while the manual is titled “power supply issues”. A pure keyword search finds nothing here, even though the answer is right there in the system. Many internal company searches fail precisely because of this gap.
On top of that, there’s the problem of ambiguous words. “Bank” can mean a financial institution, the side of a river, or a seat. A keyword search lumps everything together. A meaning-based approach recognizes from the context of the surrounding words which reading is meant. Someone searching for “bank repair car garage” won’t get financial advice offers.
This is economically relevant because search today forms the foundation of many AI products. Chatbots that answer questions about company documents must first find the right passages. If this step is done poorly, even the best language model won’t help. It will then answer fluently, but based on the wrong material.
Meaning as a point in space
Technically, an AI model translates each text into a long sequence of numbers. This sequence of numbers is called an embedding. One can imagine it as coordinates, similar to longitude and latitude on a map. Except this map doesn’t have two directions, but often several hundred.
The decisive factor is the arrangement: texts with similar meaning end up close together. “Bicycle” lies near “bike” and “cycle”, far away from “tax return”. Nobody set this arrangement by hand. It emerges during the model’s training from huge amounts of text, in which similar words appear in similar contexts.
A search then works like this: all documents are converted into such number sequences in advance and stored in a special database. When a query comes in, it is converted as well. The system searches for the points with the smallest distance and returns the corresponding texts. To keep this fast with millions of entries, not every distance is actually calculated; instead, approximation methods are used as a shortcut.
From online shops to company chatbots
In everyday life, one constantly encounters this method without it being named. Search fields in online shops suggest matching products even with an imprecise description. Streaming services find movies via a rough description of the content rather than the title. Photo search on smartphones also works this way when you type in “beach at sunset”.
In business news, the term usually appears in connection with RAG, short for Retrieval Augmented Generation. Behind this is the combination of semantic search and a language model. First, matching documents are found, then the model formulates an answer from them. Almost every corporate chatbot works according to this pattern.
A common misconception is that semantic search replaces keyword search. For part numbers, names, or legal statutes, exact matching is clearly superior. That’s why good systems combine both methods, which is called hybrid search. It is also notable that an entire industry has grown up around this technology: providers of vector databases such as Pinecone or Weaviate have raised substantial investment sums in recent years.