Glossary

Query expansion

What is Query expansion?

Query expansion automatically adds related terms to a search query so that relevant documents using different wording are also found. Typical additions are synonyms, abbreviations, word forms or translations: a search for “cancellation” might then also find texts about “termination of contract”. This is especially useful for keyword search, but it can reduce precision if too many or unsuitable terms are added. Today, language models are often used to generate the expanded queries.

Topics

search
synonyms
information retrieval

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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
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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.

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.

Did you mean (DYM)

“Did you mean” (DYM) is a search feature that suggests a corrected query when the input appears to be misspelt or unusual. It relies on similarity measures between words, language models and analysis of frequent searches. Example: someone typing “coffe machne” into an online shop sees “Did you mean: coffee machine?”. The feature reduces empty result pages and thus improves the search experience.

search
spelling correction
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