What is Semantic Search

Summary

Semantic search is a search approach that interprets the meaning and intent of a query to identify relevant information, including content that may use different wording. In artificial intelligence (AI) applications, semantic search can use natural language processing, embeddings, machine learning, and vector search to find content that is conceptually related to what a user is asking.

This article explains how semantic search works, how embeddings and vector similarity support retrieval, and how semantic search differs from keyword and natural language search. It also covers the role of large language models (LLMs), hybrid search, ranking, use cases, and factors that influence search quality.

What Does Semantic Search Mean in AI

Semantic search in AI analyzes the meaning and context of a search query to identify conceptually relevant content.

A traditional keyword search may look for documents containing the same words that appear in the query. Semantic search can identify relevant content even when the wording is different.

For example, a person may search for "how to reduce laptop startup time." A relevant document could discuss "improving boot performance" without containing the exact phrase used in the query. Semantic search uses learned relationships between concepts to identify potentially relevant content, even when the query and document use different terminology.

This ability is useful when people search using everyday language, synonyms, descriptive phrases, or questions.

Semantic search can use several AI techniques. Modern implementations commonly use embeddings to represent queries and documents numerically and vector search to identify representations that are close to one another in meaning.

The term semantic search describes the broader retrieval goal. Vector search is one technical method that can be used to achieve that goal.

How Does Semantic Search Work

A semantic search system needs a way to represent the meaning of both searchable content and user queries so they can be compared.

Content Representation

Before a search is performed, searchable content can be processed by an embedding model.

Depending on the model's capabilities, text, documents, or other supported content types can be converted into numerical representations called embeddings. An embedding contains many numerical values that represent characteristics learned from the content.

Content with related meanings tends to have more similar representations within the embedding space.

For example, content about "computer display resolution" may have a representation closer to content discussing "screen pixel dimensions" than to content about computer storage.

These embeddings can be stored in an index so they can be retrieved efficiently when a user searches.

Query Representation

When a user submits a query, the same or a compatible embedding model converts the query into a numerical representation.

The search system can then compare the query embedding with representations of indexed content.

This allows the query to be matched by meaning even when the user and the source content use different terminology.

Similarity Search

The system measures how close the query representation is to the stored content representations.

Similarity can be calculated using techniques such as cosine similarity, dot product, or other distance measures supported by the search architecture.

The search process identifies candidate content items with vector representations that are similar to the query representation, based on the configured similarity method.

This stage is commonly implemented using nearest-neighbor search because searching every stored vector individually can become expensive as a dataset grows.

Ranking and Reranking

The first retrieval stage produces candidate results, but those results may not always appear in the most useful order.

A search system can apply additional ranking signals to improve the final result set.

These signals can include semantic similarity, keyword relevance, metadata, content freshness, user context, or a separate reranking model.

Reranking allows the system to apply more detailed analysis to a smaller number of retrieved candidates before presenting the final results.

Tip: When implementing semantic search, evaluate retrieval and ranking separately. Relevant content may exist in the initial candidate set even when the final ranking does not place it near the top.

How is Semantic Search Different From Keyword Search

Keyword search and semantic search use different signals to determine whether content is relevant.

Exact Terms and Meaning

Keyword search primarily examines the words or phrases present in the query and indexed content.

A search for "wireless mouse battery" can therefore prioritize pages containing those same words.

Semantic search focuses more on conceptual similarity. It may also retrieve content discussing "battery life for cordless mice" because the meaning is closely related even though the wording differs.

Synonyms and Related Concepts

Keyword systems can support synonyms, stemming, and other techniques that expand the terms considered during retrieval.

However, those relationships usually need to be represented through search rules, dictionaries, or lexical analysis.

Semantic search uses representations generated by models trained to identify relationships between words, phrases, and concepts.

This can help with queries where several different expressions describe the same idea.

Specific Terms and Identifiers

Semantic matching does not make exact-word retrieval unnecessary.

Product numbers, error codes, personal names, acronyms, technical identifiers, and newly introduced terms may depend heavily on exact matching.

For example, a search for a specific model number may be more effectively handled through lexical matching than through semantic similarity.

This is one reason search systems can combine keyword and semantic retrieval rather than selecting only one approach.

Why are Embeddings Used in Semantic Search

Embeddings allow meaning to be represented in a form that software can compare mathematically.

Embeddings provide a numerical representation of language patterns, allowing search systems to calculate similarities between sentences and other content. An embedding model converts the sentence into a vector that represents patterns learned from language.

The important property is the relative relationship between representations.

Sentences discussing similar concepts can appear closer together in the embedding space, while unrelated concepts appear farther apart.

Embeddings can represent more than individual words. Depending on the model and architecture, they may represent sentences, paragraphs, documents, images, audio, or other information.

For semantic search, the embedding model should also match the type of content being retrieved.

An embedding that works well for general text may behave differently on highly specialized technical information, short product identifiers, or multilingual content.

Embedding quality is therefore one factor that directly affects semantic retrieval quality.

How Do LLMs Support Semantic Search

Large language models can support semantic search at several points in the retrieval process.

Semantic search and LLMs are related, but they are not the same technology. A semantic search system can operate without a generative LLM, and an LLM application can use search as one source of external information.

The term semantic LLM may refer to LLM-based systems that process semantic information or integrate semantic retrieval capabilities. In practice, several separate capabilities may be involved.

Query Understanding

An LLM can help interpret a complex user request before retrieval begins.

For example, it may extract the important concepts from a conversational question, identify implied constraints, or rewrite the request into a form that is more suitable for the underlying search system.

This can be useful when queries contain several conditions or are expressed indirectly.

Query Expansion

An LLM can produce alternative terms or related concepts that help broaden retrieval.

A query about "devices for drawing digitally," for example, could be expanded with concepts related to stylus input, pen support, or digital illustration.

Expansion should be controlled carefully because adding loosely related concepts can also reduce search precision.

Result Reranking

An LLM or another language model can analyze a limited set of retrieved candidates and estimate which ones best address the original query.

Because reranking operates on a smaller candidate set, it can apply deeper language analysis than the initial retrieval stage.

Retrieval for LLM Responses

Semantic search is also commonly used as part of retrieval-augmented generation (RAG).

A search system retrieves relevant information from a knowledge source, and that information is supplied to an LLM as context for generating a response.

The search component and the generation component therefore perform different tasks. Search identifies relevant source information, while the LLM uses the retrieved context when producing its output.

Tip: In an LLM application that uses semantic retrieval, evaluate search relevance independently from generated-answer quality. A weak response can originate from poor retrieval even when the language model itself is functioning as expected.

How is Natural Language Search Related to Semantic Search

Natural language search allows people to search using ordinary conversational language rather than carefully selected keywords or structured query syntax.

For example, a user might enter:

"Which laptops can support multiple external monitors?"

The search system needs to interpret the request instead of treating every word as an equally important search term.

Semantic search can support natural language search because it provides a way to match the meaning of a conversational query with relevant content.

However, natural language search describes how users express the query, while semantic search describes how the system can interpret and retrieve information based on meaning.

A natural language search system may use semantic retrieval, language models, entity recognition, query rewriting, keyword search, or a combination of these techniques.

The two concepts are therefore closely related but not interchangeable.

Where is Semantic Search Commonly Used

Semantic retrieval can be useful wherever people search information using concepts or natural language.

Common semantic search use cases include:

The usefulness of semantic search depends on the type of data and the way users search it. Exact identifiers and highly structured filters may still require other retrieval techniques alongside semantic matching.

Conclusion

Semantic search helps search systems retrieve information based on meaning, context, and intent. Modern semantic search implementations can use embeddings and vector search to connect queries with relevant content even when the wording does not match exactly.

Semantic retrieval can also work with natural language search, LLMs, reranking, and RAG applications. Combining semantic and keyword signals can be useful when users search with both conversational concepts and exact terms such as codes, names, or technical identifiers.

Frequently Asked Questions

What is semantic search?

Semantic search retrieves information based on the meaning and intent of a query instead of relying only on exact keyword matches.

What is semantic search in AI?

Semantic search in AI uses techniques such as natural language processing, machine learning, embeddings, and vector search to identify content that is semantically related to a query.

How does semantic search work?

Semantic search can convert queries and searchable content into embeddings, compare those numerical representations, retrieve semantically similar content, and rank the resulting matches.

What is an embedding in semantic search?

An embedding is a numerical representation of content that captures patterns related to its meaning. Semantic search can compare embeddings to identify conceptually similar information.

Is semantic search the same as vector search?

Vector search is a technique for finding similar numerical representations. Semantic search is a broader search approach that can use vector search to retrieve information based on meaning.

What is the difference between semantic search and keyword search?

Keyword search primarily matches exact or related terms, while semantic search focuses on conceptual meaning and can retrieve relevant content that uses different wording.

What is natural language search?

Natural language search allows users to express a search request using ordinary conversational language instead of formal query syntax or carefully selected keywords.

Is natural language search the same as semantic search?

Natural language search describes how a user expresses a query, while semantic search describes an approach for understanding meaning and retrieving related content.

How do LLMs support semantic search?

LLMs can support query interpretation, query rewriting, expansion, reranking, and other stages of a semantic search workflow. Semantic retrieval can also provide source information to LLMs in RAG applications.

What does semantic LLM mean?

Semantic LLM is not one specific search technology. The phrase may be used to describe LLM-based systems that work with meaning, semantic representations, or semantic retrieval.

What is hybrid search?

Hybrid search combines semantic retrieval with lexical or keyword retrieval so a search system can use both conceptual similarity and exact-term matching.

Can semantic search be used with RAG?

Yes. Semantic search can retrieve relevant information from a knowledge source and provide that information as context for a retrieval-augmented generation workflow.