Hybrid search
Hybrid search combines semantic vector similarity with traditional keyword search, then merges the two result sets, so that a retrieval system handles both conceptual questions and exact terms like product codes or error strings.
Pure vector search is weak precisely where exactness matters — part numbers, function names, proper nouns. Hybrid retrieval with a re-ranking pass over the merged candidates is usually the single largest quality improvement available to a struggling RAG system, and it is measurable on a golden set.
Related terms
- RAG (retrieval-augmented generation)
Retrieval-augmented generation is a technique where relevant documents are fetched from an external store and inserted into a language model’s prompt, so the model answers from that specific source material rather than from its training data alone.
- Vector database
A vector database stores text as numerical embeddings and retrieves entries by semantic similarity rather than keyword match, forming the retrieval layer of most RAG systems.
Tell us what you are building
Send us the problem in a paragraph. You will get a straight answer on whether we can help, and what we would do first.
Book a call