Semantic Search
LLM & Language ModelsSearch that understands meaning and intent rather than just matching keywords — 'affordable places to eat nearby' finds budget restaurants even if they don't use the word 'affordable.'
Semantic search uses embeddings to match the meaning of a query against the meaning of documents, rather than relying on exact keyword matches. This is why modern search can handle synonyms, paraphrases, and conceptual queries.
Traditional keyword search: 'cheap restaurants near me' only finds pages containing those exact words. Semantic search understands the concept and also returns results about 'budget-friendly dining,' 'affordable eateries,' and 'best value meals' — even if those exact words don't appear in the query.
Semantic search powers: AI-enhanced search engines (Perplexity, You.com), e-commerce product search, internal knowledge base search, and RAG retrieval. The technology relies on embedding models that convert text into numerical vectors where similar meanings cluster together.
Real-World Example
When Perplexity understands that your question about 'how to make my code faster' is really about performance optimization — that's semantic search understanding meaning, not just matching keywords.
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What is Semantic Search?
Search that understands meaning and intent rather than just matching keywords — 'affordable places to eat nearby' finds budget restaurants even if they don't use the word 'affordable.'
How is Semantic Search used in practice?
When Perplexity understands that your question about 'how to make my code faster' is really about performance optimization — that's semantic search understanding meaning, not just matching keywords.
What concepts are related to Semantic Search?
Key related concepts include Embedding, Vector Database, RAG (Retrieval-Augmented Generation), Natural Language Processing (NLP), Semantic Search. Understanding these together gives a more complete picture of how Semantic Search fits into the AI landscape.