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Plain-language AI glossary

Term 28TechniquesMeaning / context / connections

Techniques / Definition

Semantic Search

A search approach that finds results based on meaning rather than exact keyword matches, using embeddings to understand the intent behind queries.

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01

MeaningThe one-sentence definition.

02

ContextHow the idea works in practice.

03

UsesWhere the concept becomes useful.

01 / Plain-language context

How Semantic Search works.

Traditional search matches keywords: searching "dog food" only finds documents containing those exact words. Semantic search understands meaning: searching "what should I feed my puppy" finds documents about dog nutrition, pet food recommendations, and feeding schedules, even if they never use the exact phrase "dog food."

Semantic search works by converting both queries and documents into embedding vectors, then finding documents whose vectors are closest to the query vector. This requires: (1) an embedding model (like OpenAI's text-embedding-3-small), (2) a vector database to store and search document embeddings, and (3) a similarity metric (cosine similarity is standard).

In production, semantic search is the retrieval component of RAG systems. It powers features like "search our knowledge base," "find similar products," and "answer questions from documentation." The quality of your embedding model directly determines search quality. Hybrid search (combining semantic and keyword search) often outperforms either approach alone.

02 / Practical uses

Where it helps.

  1. 01Knowledge base search
  2. 02Product discovery and recommendations
  3. 03FAQ matching
  4. 04Document retrieval for RAG
  5. 05Support ticket routing
03 / Keep exploring
EmbeddingsNumerical vector representations of text that capture semantic meaning, enabling similarity search and clustering.Vector DatabaseA specialized database optimized for storing and searching high-dimensional vector embeddings, enabling semantic similarity search.RAG (Retrieval-Augmented Generation)A technique that enhances AI responses by retrieving relevant information from a knowledge base before generating an answer.Natural Language Processing (NLP)The branch of AI focused on enabling computers to understand, interpret, and generate human language in useful ways.