Semantic Search: Definition and Examples

Semantic search is an advanced information retrieval approach that goes beyond simple keyword matching. Instead of looking for exact occurrences of terms in a document corpus, it analyzes the deep meaning of the query and content to establish matches based on actual significance.

This technology relies on language models and vector embedding techniques. Each text — whether a user query or an indexed document — is transformed into a numerical vector in a multidimensional space. Texts with similar meanings are close in this space, allowing relevant results to be found even if the words used differ from those in the query.

In the context of generative AI and prompt engineering, semantic search plays a central role in RAG (Retrieval-Augmented Generation) architectures. It makes it possible to retrieve the most relevant passages from a knowledge base to inject into the context of an LLM, improving the accuracy and reliability of generated responses.

Unlike traditional lexical search (such as TF-IDF or BM25), semantic search understands that "electric car" and "battery vehicle" refer to the same topic. This contextual understanding makes it an indispensable tool for modern AI applications, intelligent search engines, and recommendation systems.