semantic search

basicwidespread

A search that does not look for exact matches of the typed words but tries to understand the meaning and intent of your question and provides results matching that.

In plain terms

A traditional search engine works like a book's index: if you type "car", it returns pages where that exact word appears. Semantic search is more like an experienced librarian who understands what you mean and hands you materials written with "automobile" or "passenger vehicle".

This is useful because we rarely know in advance what words the person who wrote the searched text used. You can ask in full sentences using everyday language, for example: 'what should I do if the ceiling leaks'. The system can still find the answer even if the article title is 'Repairing roof insulation defects'.

Keyword search compares letters and words, while semantic search compares meanings. This gives it its strength, but also its weakness: if you are looking for an exact part number, name, or statute number, the old, literal method is often more reliable.

Many AI chatbots and agents are built on this: when working with documents or the web, they first extract text snippets relevant to your question using semantic search.

How it works

The system pre-processes searchable texts: it breaks them into smaller parts and uses a language model trained for this to create a number sequence from each that describes the meaning of the text. This number sequence is called an embedding.
These number sequences are points on a 'semantic map': texts with similar meanings end up close together, while those on different topics are far apart.
When you enter your question, it is also converted into a sequence of numbers, meaning your question becomes a point on the same map.
The search engine selects the points closest to your question and returns the texts associated with them as results.
Better systems also combine this with keyword search (hybrid search), and at the end, a separate model re-ranks the results so the best ones appear first.

An everyday example

You type 'warm shoes for a winter hike' into an online store's search bar. None of these words appear in the product names, yet you receive lined hiking boots because the search engine understood your intent, not just matched letters.

Who is affected

everyoneWeb search engines, online shops and AI chatbots increasingly search this way, so you can also ask in natural sentences.

marketerRepeating keywords is becoming less valuable: content must clearly and fully answer what the reader is curious about. → Marketing

entrepreneur, executiveA corporate document repository, knowledge base, or customer service becomes searchable even if staff and customers use different words than the documents.

developerThis is the basis for document-based AI applications and for an agent finding the information needed for a task. → Code

A common misconception

MythA semantic search engine understands my question like a human, so its results are certainly correct and relevant.

In realityThe system calculates semantic similarity; it does not verify truth. Content that resembles your query may be outdated, incorrect, or even state the opposite.

Limits and risks

It is often weaker than literal search for precise identifiers (part number, name, paragraph number, rare technical term).
It is hard to say why it produced that specific result, making its errors harder to spot and fix.
Its quality depends on the underlying language model: it may perform worse on Hungarian and narrow-domain texts than on general English content.
Negation and subtle differences are easily overlooked: the texts "lactose-free" and "contains lactose" are semantically close, so both may appear.

Similar, but not the same

keyword search – Keyword search looks for exact occurrences of the entered words, while semantic search looks for the meaning behind them.
vector search – Vector search is the technical tool (searching for proximity of number sequences), while semantic search is the goal, which is mostly achieved with it.
generative search – Semantic search returns results, while generative search also formulates an answer in its own words based on these.

Related concepts

Broader

information retrieval, Natural language processing, search engine

Narrower

vector search

On the edge

hybrid search, re-ranking, embedding, vector database, RAG, knowledge graph, generative search engine optimization

A brief history

An early milestone of meaning-based (semantic) search was Deerwester and colleagues' 1990 study on latent semantic analysis. Google made it known to the general public: it introduced the Knowledge Graph in 2012 and the BERT language model in its search engine in 2019. In the same year, Sentence-BERT appeared, becoming one of the foundations of today's embedding-based semantic search.

Where things stand

At the end of 2026, the search engines of major websites and online shops typically work in a semantic or hybrid way, and this is increasingly common in corporate knowledge bases too. Document search in AI chatbots and agents is also largely built on it.

Status: 2026 November

Also known as

semantic search · semantic search · semantic search · semantic search · neural search · embedding-based search

Sources

This entry was machine-edited and has undergone machine expert review; the “In the news” section updates automatically from Agent Age stories.