Query Rewriting
In one line
Query rewriting is the step where the search system rephrases a user's prompt into a form better suited for retrieval — typically the first stage of an AI search pipeline.
Going deeper
Query rewriting is the step where the system rephrases the user's prompt into a form better suited for retrieval. It can mean disambiguating vague phrasing, adding synonyms or related terms, or expanding a terse keyword into a natural-language question.
The takeaway for marketers is that 'what the user typed' and 'what the model actually searches for' are no longer the same string. Content that covers the same intent in several phrasings tends to be cited more reliably than content that bets on one exact keyword.
Search engines were already doing some version of this, but in AI search it is far more aggressive — the LLM itself often takes a pass at unpacking the user's intent. A lot of the 'keyword-level SEO doesn't really work on AI search' feeling traces back to this layer.
Related terms
Query Fan-Out
Query fan-out is the mechanism where an AI search engine expands a single user query into multiple sub-queries, runs them in parallel, then composes a single answer from the combined results.
GEO·AEOQuery Decomposition
Query decomposition is the technique of breaking a complex question into smaller, easier sub-questions — a building block used widely across AI search and LLM reasoning.
LLMRAG
RAG (Retrieval-Augmented Generation) lets an LLM fetch external documents at answer time and ground its response in them — the technique behind ChatGPT Search, Perplexity and most AI search products.
LLMEmbedding
An embedding is a numeric vector representation of text or other data that preserves semantic meaning — the foundation of semantic search, vector databases and RAG.
GEO·AEOGEO
GEO (Generative Engine Optimization) is the practice of optimizing content and data so that a brand gets cited and recommended inside generative AI search answers like ChatGPT, Perplexity and Google AI Overviews.
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