How Can Brands Optimize for ChatGPT's Rewritten Queries?

What changed in the understanding of ChatGPT citations?

The important change is not a confirmed ChatGPT product release. It is a stronger explanation of how ChatGPT citations may be selected when the model uses web search. A University of Toronto research preprint examined 158,099 answers and 468,037 citations across ChatGPT, Gemini, and Perplexity over two 90-day collections of commercial questions. Its central finding is blunt: within a website, a page has to be retrieved before it can be cited.

The researchers replayed questions through the engines' APIs and found that every ChatGPT-cited page in their dataset had been retrieved by ChatGPT's own search. Those searches used sub-queries rewritten by the model for each answer. The paper says those sub-queries rarely coincided with Google or Bing results. That creates a different editorial problem from classic keyword targeting. A page can be clear, technically sound, and optimized around a visible keyword, yet still miss the wording or meaning of the query that ChatGPT actually sends to search.

That distinction matters because teams often treat a citation as a simple reward for publishing a good page. Good pages still matter. The study found that titles and paragraphs matching the questions people ask were associated with citations. But the apparent mechanism was retrieval through the engine's rewritten queries, not a universal lift from familiar page features.

This is a news reaction, not a claim that one paper settles every ranking system. The paper is a preprint submitted to arXiv on 2026-10-10, and its results concern the collection and methods described by its authors. Still, it has a useful working model for brands tracking Citation Share: answer quality gets a page into contention, while retrieval determines whether that answer can enter the citation set.

1. Before: teams could treat on-page has as likely direct levers for ChatGPT citations.
2. After: within-site citation differences may be better explained by whether ChatGPT's rewritten search retrieves the page.
3. What to do: publish direct answers that cover the semantic variants around a buyer question, then measure whether the brand appears across that question set.

What does the study say about page content and retrieval?

The study says content still matters, but primarily when it helps a page match the meaning of a rewritten query. Its authors report that page properties explained 2.8% of the variation in which pages ChatGPT cited within a website. Retrieval by ChatGPT's own rewritten searches explained 32%. The reported 95% confidence interval for that retrieval result was 24% to 42%.

That is not permission to strip pages down to keyword variations. Search retrieval needs a page that makes a relevant claim legible, specific, and supported. A vague category page gives an engine little to match and little to cite. A page that clearly defines the problem, names the use case, answers a precise question, and supports its conclusion gives retrieval systems more useful surfaces.

The paper also separates within-site effects from across-site effects. FAQ blocks, structured data, summary boxes, and page speed appeared effective when comparing different websites, according to the abstract, but not when comparing pages inside the same site. That is a warning against turning one study into a checklist of tricks. The authors do not present a hack for citations. They present evidence that content needs to align with an engine's route to finding it.

OpenAI's web-search documentation supports the broader operational boundary. It describes web search as a tool models can use to access current information and produce sourced citations, while noting that a model can choose whether to search based on the input. That means a brand cannot assume one prompt, one page, or one observed answer is the full system.

1. Write the title around the reader's decision or question.
2. Put the direct answer in the opening paragraph.
3. Use supporting sections to cover adjacent buyer questions without padding the page.
4. Keep sources close to the claims they support.

Who does rewritten-query behavior affect most?

Rewritten-query behavior affects brands whose pipeline depends on being named when buyers ask category, comparison, or local-service questions. For a B2B SaaS team, the risk is being absent when someone asks ChatGPT for the best tool for a defined job. For a multi-location service business, the risk is being absent when a person asks for the best service in a city. In both cases, the buyer may receive one compact answer rather than a long results page.

It also affects editorial teams that measure only Google rankings and organic sessions. Those metrics remain useful, but they do not show whether ChatGPT cites a brand, which questions trigger a citation, or which competitors appear beside it. A brand can have pages that perform in conventional search while failing to cover the questions an answer engine reformulates and retrieves.

The study's brand-knowledge experiments add another reason to separate page performance from brand visibility. The authors report that, with identical product facts, revealing a brand name changed ChatGPT's top-three ranking rate 2.1 times as much as merely repeating the prompt. For Gemini, the reported difference was 3.3 times. The result does not prove that brands can manufacture recognition through a page edit. It shows that model knowledge and retrieval can both shape who gets named.

That makes measurement essential. A growth team needs a stable prompt set, a named competitor set, and a record of citations over time. Omnicite calls the percentage of relevant AI answers in a category that cite a brand Citation Share. It is a clearer operating metric than treating one favorable ChatGPT response as a win.

1. B2B SaaS teams should test category, alternative, comparison, and use-case questions.
2. Local businesses should test service and location combinations that reflect buyer language.
3. Content teams should identify pages that answer a question well but do not appear in cited answers.
4. Brand teams should separate recalled brand presence from page-level citation evidence.

How should brands optimize for rewritten ChatGPT queries?

Brands should optimize for question coverage and semantic precision, not for imagined secret keywords. Start with the commercial questions customers ask before they know your product name. Then map each question to a page that answers it directly. If several questions require materially different answers, create separate pages. If they share one answer, keep them together and make the relationship explicit.

The first practical move is to review pages as retrieval candidates. Can a search system identify the audience, job, constraint, and answer from the title and first paragraphs? Does the page use the terms a buyer would use when asking for help? Does it explain the category in plain language before presenting a brand claim? Pages built around internal company vocabulary often fail this test because the buyer and the model are likely to use different words.

Next, build depth around the decision instead of decorating the page with generic SEO components. A comparison should state who each option fits, where the differences matter, and what evidence supports the distinction. A how-to should give a complete route from the question to an outcome. A definition should resolve the term quickly, then explain why it changes a decision. This is Citation Engineering in practice: authoritative coverage that makes a page relevant to a wider, defensible set of questions.

Finally, protect freshness where the claim can change. Product details, regulations, pricing, benchmarks, and current recommendations should be reviewed against their source. The study does not say that recency alone earns a citation. It does show why stale or thin pages can lose a retrieval contest when the engine rewrites a question toward a more specific current need.

1. Map each priority buyer question to one clear primary page.
2. Use titles and opening paragraphs that state the audience, task, and answer.
3. Add only related sections that help resolve a real adjacent question.
4. Cite dated primary sources for facts that can change.
5. Update pages when the evidence or buyer question changes.

What should brands stop doing?

Brands should stop treating a single template element as a citation strategy. The new paper found limited within-site evidence that FAQ blocks, structured data, summary boxes, or page speed explain which pages ChatGPT cites. These elements can still have legitimate usability or technical roles. They are not a substitute for being retrievable for the question the engine chooses to search.

They should also stop writing for a single exact prompt. ChatGPT may rewrite a user request into several sub-queries, and the paper says those rewritten queries are made on every answer in the authors' replayed tests. An article designed to match one frozen phrase can be brittle. A stronger page explains the concept with the terms, constraints, and outcomes that naturally surround the question.

Do not infer causation from an isolated citation. AI answers vary. Engines change. Query wording changes. A citation can also reflect model knowledge rather than a newly retrieved page, according to the study's findings. The right response is repeated measurement across a controlled prompt universe, not celebrating screenshots.

Most of all, do not promise that a content change will produce a specific number of citations. The evidence supports a better operating hypothesis: useful, complete, source-backed content improves a brand's chance to match relevant retrieval paths. It does not create a guaranteed outcome.

1. Do not publish FAQ blocks solely to chase a citation.
2. Do not optimize around one screenshot or one prompt.
3. Do not confuse a brand mention with proof that a page was retrieved.
4. Do not claim that any edit guarantees ChatGPT citations.

How can a team measure whether its response is working?

A team can measure progress by tracking the questions that matter and recording the sources each engine cites. Begin with a focused set of commercial prompts. Include category questions, comparison questions, use-case questions, and location variants where relevant. Record the answer, cited domains, cited pages, named brands, and date. Re-run the set consistently rather than changing the prompts every week.

Use more than one metric. Citation Share shows the percentage of relevant answers that cite the brand. Citation Count per day captures volume. Answer Presence shows breadth across the question universe. Share of Voice compares the brand with named competitors. Together, these metrics separate a brand that appears occasionally from one that is reliably considered when the category is discussed.

When a page is not cited, diagnose the coverage before rewriting blindly. Is there a page for the underlying question? Does it answer the task at the right level of specificity? Is the source support current? Is a competitor's page clearer about the decision? Then publish or improve the page with a concrete evidence standard. The objective is not to stuff in every possible phrase. It is to give the engine a page worth retrieving for a real buyer question.

The durable lesson from this research is simple. Rankings got you found. Citations get you chosen. If rewritten search queries stand between a question and a citation, the work is to become the clearest source for the full set of questions behind that answer.

1. Create a fixed prompt set around revenue-relevant questions.
2. Record citations, named competitors, and the cited URLs on every run.
3. Compare Citation Share with Answer Presence before judging a content change.
4. Review uncited questions for missing or weak answer coverage.
5. Use the findings to prioritize the next evidence-backed page.

Sources

Source: arXiv

The preprint reports 158,099 answers, 468,037 citations, 32% within-site citation variation explained by rewritten-query retrieval, and 2.8% explained by page properties. arXiv, 2026-10-10

Source: Anything Engine Optimization

The news report summarizes the University of Toronto preprint and reports the same retrieval and page-property figures. Anything Engine Optimization, 2026-10-13

Source: OpenAI Developers

OpenAI documentation describes web search as a tool models can use to access current information and provide sourced citations. OpenAI Developers, 2026-10-15