Glossary

Document reranking

What is Document reranking?

Document reranking is a second sorting step in search and RAG systems: a fast initial search returns a set of candidate results, which a more precise but more costly method then reorders by relevance. This is often done by a so-called cross-encoder that assesses the question and a passage together. As a result, the most relevant passages end up at the top before a language model formulates an answer from them.

How KI.ordo helps

KI.ordo's knowledge search ranks passages by relevance using the BM25 keyword method directly on the server.

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Topics

search
RAG
relevance

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Related terms

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

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 pipeline

A retrieval pipeline is the sequence of processing steps a system uses to find the right information for a question. Typical steps are: ingesting and cleaning documents, splitting them into chunks, building a search index, optionally rewriting the query, retrieving matches, ranking them (reranking) and passing the best ones to the language model. It is the foundation of every RAG application, and its quality largely determines how good the resulting answers are.

Document grading (RAG)

In retrieval-augmented generation (RAG), document grading means checking retrieved passages to see whether they match the question and can support an answer. Only passages rated as relevant are passed to the language model; unsuitable results are discarded or trigger a new search. This reduces the risk of invented or wrongly sourced answers, for instance when an outdated document turns up for a question about current prices.

RAG
relevance
+1