Semantic search

Semantic search turns questions and document passages into embedding vectors. Texts that are close in that vector space can share a topic or intent even when they do not use the same words.

A question about an annual-leave request can miss a document that calls the process a holiday application if search only matches words. Semantic retrieval uses contextual proximity to locate the related procedure.

Keyword search quickly ranks exact or weighted terms and is strong for names, identifiers, and codes. Semantic search can improve recall when the question uses synonyms or a different phrasing.

The best result need not come from one method alone. KobiGPT’s hybrid Qdrant collection combines a dense embedding signal with a sparse lexical signal, bringing meaning and exact-term strengths together.

A retrieval score is a ranking signal, not proof. The top passage may be close to the question but miss a critical exception; read the source text and check its freshness before accepting the answer.

Clear headings, short sections, and correct department scope make retrieval easier. A large pile of unrelated files can bury the advantage of semantic search in noise.

A useful way to evaluate semantic-search is to follow one real question from the source document to the final answer. Record which file was selected, what context reached the model, and what a reviewer would need to verify. This turns a definition into an operational check and makes the result comparable across teams.

The same check should include ownership and change management. Decide who updates the relevant documents, how an outdated result is reported, and which access boundary applies. KobiGPT can provide the assistant and the cited document context, but the organisation still owns the source material, permissions, and the decision made from the answer.