gensai.aiSemantic search retrieves content by meaning rather than by exact keywords: a query about “paid leave” will surface a document mentioning “holiday entitlement”. It relies on embeddings and vector databases, and is the first step of any RAG pipeline.
In practice at Gensai
It is the foundation of the natural-language property search engine built by Gensai.
When does semantic search make the difference?
When users phrase their questions in their own words, which do not match those of the documents: customers facing a catalogue, employees facing an internal document base, citizens facing regulatory texts. Combined with keyword search and reranking, it generally gives the most relevant results.
Common mistake
Dropping keyword search. For a product reference, an item number or a proper name, exact matching remains essential. The best engines combine both approaches.
Same theme
Definition written with AI assistance, checked by Sébastien Lecocq, co-founder of Gensai.
Going beyond the definition?
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Related services: Chatbots and answer engines