AI rewrites the question before searching: is your content ready?

When someone asks an AI a question, the system internally reformulates it in three, four, five different ways before searching for the best sources. If your content only answers the exact phrasing you imagined, you are intercepting a small slice of the real possibilities. Your competitors who cover the most likely variants of the same question get found far more often — with the same underlying content. Covering the right reformulations is precise work, and it can multiply the number of times you get cited without creating anything new.

The user writes: “how do I show up on ChatGPT”. The AI system doesn’t search for that exact phrase. It rewrites it into several variants — “how to appear in AI answers”, “optimization for ChatGPT”, “brand visibility artificial intelligence” — and searches all the variants in parallel.

If your content only answers the literal phrasing of the question, you are capturing a fraction of the possibilities. All the reformulations slip past you — and with them, all the citations you could have earned.

This mechanism is called Query Rewriting, and it is one of the most underrated steps of the RAG pipeline. It is not an accessory feature: it is the first transformation that happens to the user’s question, before the system even goes looking for anything. If you don’t understand it, you are optimizing for a scenario that doesn’t exist.

Why the original query isn’t enough

In the world of retrieval-augmented research, the problem with the original query is documented precisely. As Ma et al. (2023) write in the paper that introduces the Rewrite-Retrieve-Read framework:

“There is inevitably a gap between the input text and the needed knowledge in retrieval.”

This gap is not a flaw in the system — it is a structural feature. The user phrases the question in their own language, with their own ambiguities and implicit context. The retrieval system works on a corpus written in a different language, more technical or simply organized around concepts the user doesn’t use.

Ma et al. (2023) propose shifting the focus from adapting the retriever or the reader to adapting the query itself. The framework they describe, Rewrite-Retrieve-Read, reverses the traditional order: first you rewrite, then you retrieve, then you read. This is not an implementation detail — it is a paradigm shift about where the intervention happens.

The practical consequence is direct: it is not enough to answer the question well as it is phrased. You have to answer the question well as it will be searched after the reformulation.

How Query Rewriting works in the RAG pipeline

Query Rewriting is a phase of the RAG pipeline in which the system transforms the user’s question before querying the index. It does so in three main ways.

Semantic expansion: the original query is enriched with synonyms, terminological variants and alternative phrasings. “How to show up on ChatGPT” also becomes “how to appear in language model answers” and “AI brand visibility”. The system generates broader versions to increase the probability of finding relevant documents that use different terminology.

Disambiguation: when the query is ambiguous, the system generates variants that resolve the possible ambiguities. “Apple” is treated both as a technology company and as a fruit, depending on the context. “Optimization” can refer to traditional SEO or to machine learning. The system doesn’t assume — it explores.

Decomposition: a complex query is broken down into simpler sub-queries that can be retrieved separately. “What is the difference between RAG and fine-tuning and which one is better to use for AI visibility” becomes three distinct questions, each with its own retrieval cycle.

The literature documents it clearly. In the survey by Gao et al. (2024):

“The original queries are not always optimal for LLM retrieval, especially in real-world scenarios. Therefore, we can prompt LLM to rewrite the queries. In addition to using LLM for query rewriting, specialized smaller language models, such as RRR (Rewrite-retrieve-read), can be adopted as a trainable rewriter.”

The point is significant: the rewriter is not necessarily the same model that generates the final answer. They can be smaller models, trained specifically for this task. Which means the reformulation is a step critical enough to deserve a dedicated model.

Systems like Perplexity and Google AI Overview typically generate several reformulations for each query and search for sources for each one. The pool of results is then unified and passed to the reranker, which selects the most relevant documents across the combined set. Your content doesn’t compete only for the original query — it competes for all the reformulations the system generates.

The case of hypothetical documents

There is a variant of Query Rewriting that deserves separate attention because it works in a counterintuitive way. Gao et al. (2024) describe it like this:

“Another query transformation method is to use prompt engineering to let LLM generate a query based on the original query for subsequent retrieval. HyDE construct hypothetical documents. It focuses on embedding similarity from answer to answer rather than seeking embedding similarity for the problem or query.”

HyDE — Hypothetical Document Embeddings — is an approach in which the system doesn’t transform the query into another query, but generates a hypothetical document that would answer the question. It then uses the embedding of that imaginary document to search for similar real documents.

The consequence is technical but important: the comparison happens between two answers, not between a question and an answer. The system searches for documents that resemble what the answer should look like, not documents that contain the words of the question.

It follows that your content must be structurally similar to complete answers — not product descriptions, not category pages, not promotional copy. A document that answers a specific question directly and completely is more likely to be retrieved by a HyDE system than a document that describes a service. The shape of the answer matters as much as the content.

The concrete scenario: what happens to your page

A marketing manager searches on Perplexity: “why doesn’t my brand show up on ChatGPT?”

The system rewrites the query into four variants:

• “brand not visible ChatGPT reasons”
• “how to increase brand visibility in AI answers”
• “brand optimization for language models”
• “why doesn’t ChatGPT mention my brand”

For each one, the system runs a separate retrieval cycle, based on BM25 and hybrid search. The chunks retrieved from each reformulation are aggregated and passed to the reranker.

Your content contains “brand”, “ChatGPT” and “visibility” but does not contain “language models” or “optimization”. You get found for two reformulations out of four. Your competitor has a page that uses both “ChatGPT” and “language models”, both “visibility” and “optimization”, both “brand” and “company”. It gets found for all four reformulations.

At equal quality, the competitor gets cited more often. Not because its content is better — but because its semantic coverage is broader. The vocabulary you write with determines which reformulations you intercept.

How to adapt your content to Query Rewriting

The starting point is to stop optimizing for a single phrasing and start optimizing for a semantic field.

• Map the reformulations for each key page: identify the main query and then list 6-8 different ways a user might ask it. Use Google Suggest, People Also Ask, related searches. Run the same search on Perplexity with different phrasings: if the sources change, you are seeing Query Rewriting in action.
• Check your semantic coverage: does the page text naturally contain the keywords of the main reformulations? If your page talks about “SEO consulting” but never about “search engine optimization”, you are losing an obvious reformulation. Run a text search (Ctrl+F) for each variant you mapped.
• Use headings as systematic reformulations: each section title can be a variant of the main query. The page title answers the question as the user phrases it, the internal headings answer the reformulations as the system generates them. This way each chunk of the document is optimized for a different variant — which also improves chunk retrieval at a granular level.
• Integrate the variants into the body text in a narrative way: not lists of synonyms, not mechanical repetitions — flowing sentences that use the variants in context. “SEO consulting — or search engine optimization, as it is called in technical contexts — is the process by which…” covers two reformulations in one natural sentence.
• Structure FAQs as explicit reformulations: a FAQ section is, technically, a list of reformulations of the same thematic area. “How do I appear on ChatGPT?”, “Why doesn’t my brand show up in AI answers?”, “What does it take to be cited by Perplexity?” — three distinct questions that cover different terminological variants and increase the interceptable surface.
• The shape of answers, not just vocabulary: as the HyDE mechanism suggests, retrieval systems compare answer embeddings with answer embeddings. Content that answers a specific question directly and completely — with a clear structure, without ambiguity, with the main point in the first two paragraphs — is structurally more similar to the hypothetical document the system generated internally.

The practical test to run today

For each key page on your site:

1. Write the main query the page answers
2. List 5 reformulations of that query — at least two with technical terminology, at least two with everyday language
3. Search for each reformulation in the page text
4. Count for how many reformulations the page contains the main keywords

If coverage is below 60% — three reformulations out of five not found — you are losing visibility not for lack of quality, but for lack of semantic breadth.

Query Rewriting is not something you can control — it is the system that decides how to reformulate. But you can control your semantic coverage: how much of your vocabulary overlaps with the most likely reformulations. The broader the overlap, the more reformulations you intercept, the more citations you earn.