Query Rewrite

When you’re searching for something, you usually just type in a few words. “Weather tomorrow” or “that thing with customs and cars”. For a human, that’s understandable, but for a search system it’s often not enough. Query rewrite means: the system secretly rewrites your query beforehand. From “that thing with customs and cars” becomes, for example, “new import tariffs on cars 2025”. Only with this rewritten version does the actual search then take place. You usually don’t notice any of this, you just notice that the results fit better.

Why brief questions yield poor results

Many search systems essentially compare words or meanings of texts. If your query uses different words than the sought-after document, the match is lost. A manual talks about “accumulator”, you type “battery dead”. Without rewriting, the system may not find the right page at all. Query rewrite closes exactly this gap between everyday language and technical language.

This is especially important for chatbots that look things up in documents before answering. This lookup is called retrieval, meaning the fetching of matching text passages from a database. Whatever isn’t found in this process, the model also can’t use in its answer. A poor search query therefore directly leads to a poor or fabricated answer. Conversely, a well-rewritten query often achieves more than a larger language model.

Online retail is also about money. Someone who types “sneakers red size 9” and sees nothing suitable won’t buy. Shops therefore rewrite queries, correct typos, and add synonyms like “trainers”.

From typos to broken-down questions

The simplest form works with fixed rules and word lists. Typos are corrected, abbreviations are expanded, synonyms are appended. This is fast and cheap, but it doesn’t understand any context. That’s why language models now often take over this task. They receive the query plus the previous messages of the conversation and formulate an independent question from it.

A common case is resolving references. You ask “How expensive is the model?” after a specific car was discussed earlier. On its own, this question doesn’t state which car is meant. The rewrite adds the name so that the search works even without the conversation history.

A second technique breaks down complicated questions into several simple ones. From “Which company grew more in 2024, A or B?” two separate search queries are created. Some systems also deliberately generate three or four variants of the same question and search with all of them in parallel. Afterward, the found text passages are merged and sorted by relevance. The price for this is additional computation steps and thus somewhat longer wait times.

Query rewrite in search engines, shops, and AI assistants

At Google and other search engines, query rewrite has been running in the background for years. The suggestions below the search field and notices like “People also searched for ...” are visible relatives of this. The message “Search instead for:” in the case of a typo also belongs to this family.

In enterprise products, you mostly encounter this term in connection with internal knowledge bases. A support chatbot is supposed to search manuals and old tickets. Developers then measure how many correct documents are found, with and without rewrite. This number often determines whether such an assistant is even usable at all.

A typical misconception is confusing query rewrite with rewriting the answer. Only the search query is changed, not the output text. And more rewriting isn’t automatically better: if you alter a query too heavily, you end up searching for something the user never meant.