AI Glossarytechniques

Semantic Search

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

How It 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.

Common Use Cases

  • 1Knowledge base search
  • 2Product discovery and recommendations
  • 3FAQ matching
  • 4Document retrieval for RAG
  • 5Support ticket routing

Related Terms

RAG (Retrieval-Augmented Generation)

A technique that enhances AI responses by retrieving relevant information from a knowledge base before generating an answer.

Embeddings

Numerical vector representations of text that capture semantic meaning, enabling similarity search and clustering.

Vector Database

A specialized database optimized for storing and searching high-dimensional vector embeddings, enabling semantic similarity search.

Natural Language Processing (NLP)

The branch of AI focused on enabling computers to understand, interpret, and generate human language in useful ways.

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