Definition

Semantic search is how AI finds results based on what you mean, not just what you type. Instead of looking for exact word matches, it uses embeddings to understand the meaning and gives smarter results.

Example

If you search ‘how to fix a bike tire,’ semantic search can find pages titled ‘bike repair tips’ or ‘patching a flat.’

How It’s Used in AI

Used in search engines, chatbots, help desks, and recommendation systems. It makes search feel more natural and useful—especially in tools powered by LLMs or vector databases. Semantic search is often part of Retrieval-Augmented Generation (RAG) workflows in AI apps.

Brief History

Semantic search began to grow in the 2010s alongside NLP breakthroughs. As embeddings became more powerful, tools like Google Search, Elasticsearch, and OpenAI models began using it to deliver better, context-aware results.

Key Tools or Models

Popular tools include Pinecone, Weaviate, Elasticsearch with dense vectors, and OpenAI Embeddings. AI apps like ChatGPT with browsing use semantic search to improve relevance.

Pro Tip

Great semantic search depends on strong embeddings. Improve your AI’s answers by pairing good data with the right vector search tools.