Glossary

AI Search

What is AI Search?

AI search refers to search methods that use AI to grasp the intent behind a query, weight results and often generate a summarised answer with sources rather than a plain list of links. Technically, they use keyword search, vector search over embeddings or a combination, frequently followed by a language model. Examples include answer engines on the web and internal knowledge search that helps staff find what they need in manuals and policies.

How KI.ordo helps

KI.ordo searches stored knowledge on the server using BM25 keyword search and does not use vector or embedding search.

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Topics

Search
Knowledge
Retrieval

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← All terms

Related terms

Information Retrieval

Information retrieval covers methods that find the documents or passages in a large body of text that match a query and rank them by relevance. Classic approaches such as BM25 weight search terms by how often and how rarely they occur; newer ones add semantic similarity. It underpins search engines, enterprise search and retrieval-augmented generation, in which a language model uses the retrieved passages as the basis for its answer.

Search
BM25
+1

Retrieval-augmented generation (RAG)

Retrieval-augmented generation combines search with text generation: before answering, the system searches a knowledge collection – such as manuals, policies or FAQs – for relevant passages and passes them to the language model as context. The answer is therefore based on current, company-specific content rather than only on the model's training knowledge. This reduces invented statements, makes sources traceable and allows updates without retraining.

RAG
knowledge search
+1

Embedding vector

An embedding vector (embedding) is a list of numbers that represents the meaning of a word, sentence or document so that texts with similar content lie close together. This makes it possible to recognise, for example, that 'cancellation' and 'ending the contract' are related even though they share no words. Embeddings underpin many semantic search systems, recommendation services and RAG applications; they are computed by specially trained models.

vector
semantic search
+1

Generative engine optimisation (GEO)

Generative engine optimisation (GEO) covers measures that help content become visible in, and be cited as a source by, AI-powered search systems such as ChatGPT, Perplexity or the AI overviews in search engines. The term comes from a 2023 research paper. Key factors include clear, directly quotable answers, a clean structure, evidenced facts, up-to-date information and identifiable authors. GEO complements classic search engine optimisation (SEO) rather than replacing it.

AI search
SEO
+1

Template