AI Engines and Surfaces

Semantic Search

Semantic search is a retrieval technique that matches a query to documents by the meaning of the text rather than by exact keywords, usually by converting both the query and the documents into vector embeddings and finding the closest matches.

Also known as:vector search, meaning-based search, embedding search

Classical keyword search ranks documents by how literally they match the terms in the query. Semantic search instead represents each piece of text as a high-dimensional vector that captures its meaning, then ranks documents by how close their vectors are to the query vector. The result is that a query like "ways to lower customer churn" can match a page that talks about "reducing subscription cancellations" even though no keyword overlaps.

Most modern AI retrieval pipelines use semantic search as their first or second stage. They embed every document chunk into a vector index, embed each incoming query, retrieve the nearest chunks and then feed them to a language model to generate an answer. This is the retrieval half of retrieval-augmented generation.

For content authors, semantic search reduces the importance of exact keyword matching and increases the importance of clear, well-scoped writing. A page that addresses one specific topic in plain language tends to embed cleanly and rank well in semantic retrieval. A page that hedges or covers ten topics at once produces a less distinctive embedding and is harder to match precisely.

Key points

Matches on meaning rather than literal keyword overlap.
Built on vector embeddings of both queries and documents.
Used as the retrieval stage in most modern RAG systems.
Rewards content that addresses one specific topic clearly.

Frequently asked questions

How does semantic search differ from keyword search?

Keyword search matches the literal words in a query. Semantic search converts the query and the documents into vector embeddings and matches them by meaning, so paraphrases and synonyms can still match.

Does semantic search replace SEO keywords?

It changes the emphasis. Keywords still help, but pages also need to be topically clear, well-scoped and written in natural language so the embedding captures the meaning cleanly.

Related terms

Vector Embedding

A vector embedding is a numerical representation of a piece of text (or other data) as a list of numbers in a high-dimensional space, designed so that texts with similar meaning end up close together and can be compared by a fast distance calculation.

Retrieval-Augmented Generation (RAG)

Retrieval-augmented generation (RAG) is an AI architecture that first retrieves relevant documents from an external source and then feeds them to a language model so the model can ground its answer in those documents rather than relying only on what it memorized during training.

Answer Engine

An answer engine is a search or chat product that returns a direct synthesized answer to a question instead of (or in addition to) a list of links, with ChatGPT, Perplexity, Google AI Overviews, Gemini and Copilot being the most widely used examples.

Large Language Model (LLM)

A large language model (LLM) is a machine learning model trained on huge amounts of text to predict the next token in a sequence, which lets it generate fluent natural-language responses and power products such as ChatGPT, Perplexity, Gemini and Copilot.

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