How to Optimize for ChatGPT's Rewritten Queries

What changed in how we should think about ChatGPT citations?

What changed is the evidence behind a familiar but poorly specified problem: a good page can still miss a ChatGPT citation if ChatGPT Search does not retrieve it for the query it actually sends. The October 10, 2026 preprint by Mahe Chen and Nick Koudas studied 158,099 answers and 468,037 citations across ChatGPT, Gemini, and Perplexity in two 90-day collections of commercial questions. It reports that, when comparing pages within the same website, measured page properties explained 2.8% of which pages ChatGPT cited, while retrieval by ChatGPT's own search explained 32%. The paper describes the difference as a retrieval problem, not a license to abandon content quality.

This is not a confirmed product announcement from OpenAI, and it is not evidence that a technical setting has suddenly changed for every publisher. It is a new, public research result about how citations behaved in the researchers' collection. That distinction matters. A team should not rebuild its site around one preprint. It should update the hypothesis it tests: the important question is no longer only whether a page looks optimized after retrieval. It is whether the page is eligible to be retrieved for the engine's reformulated search.

The study reports that ChatGPT rewrites sub-queries on every answer and that those results rarely coincide with Google or Bing results. If that holds for your category, a page designed only around one conventional keyword can be too narrow. It may satisfy the phrase typed by a user while failing to make a clear match for the product category, use case, comparison, constraint, or buyer outcome embedded in the engine's query. The work moves the practical focus from surface checklists toward Citation Share: the share of relevant AI answers in a category that cite your brand.

The implication is blunt. FAQ blocks, schema, summary boxes, and page-speed work can still be useful site work, but they are not substitutes for being retrieved. The preprint says those properties appeared effective across websites but did not explain much variation between pages on the same site. A content program that keeps publishing near-duplicate pages with better markup may produce cleaner pages without producing materially more ChatGPT citations.

1. Do not describe the result as a universal ChatGPT algorithm update.
2. Do treat retrieval for rewritten queries as a measurable editorial hypothesis.
3. Do separate site hygiene work from the work of covering an answer space.
4. Do keep the evidence boundary clear: this is a submitted preprint, not a vendor specification.

Who does this affect most?

This affects teams competing for ChatGPT citations in crowded commercial categories, especially B2B software, local services, and comparison-heavy markets. These are the situations where an answer engine has to select a small number of sources from many plausible pages. Omnicite calls the outcome Citation Share because the competitive question is not only whether a page can appear. It is how often your brand is cited across the relevant answer universe.

B2B SaaS teams are likely to feel the change in pages built around category, integration, implementation, pricing, and alternative queries. A buyer may ask a broad question, but an engine can decompose that request into narrower searches around a workflow or requirement. A thin page that repeats the category term can lose to a page that explains the decision with clear product facts, boundaries, and supporting detail. The study also reports that brand knowledge can influence results, so content alone cannot explain every citation or recommendation.

Local and multi-location businesses face a similar issue in a different form. A user asking for a service in a city can generate a query with service, geography, eligibility, urgency, or proof needs. Location pages that simply swap city names into the same paragraph do not give an engine much distinct material to retrieve. Pages should instead make the local service, service area, evidence, and contact route legible without inflating claims.

The result also affects analytics teams. A rank tracker can show stable conventional search positions while ChatGPT citations change because the retrieval path is different. That is why reporting needs answer-level observation across engines. Track Citation Count for daily volume, Answer Presence for breadth, and Share of Voice for competitor comparison. None of those metrics proves causality by itself, but together they expose where the answer engine is selecting someone else.

1. Growth teams responsible for category and comparison pages.
2. Editorial teams publishing scaled content across a topic cluster.
3. Local businesses competing for service and geography questions.
4. Analytics owners who need to distinguish search visibility from citation visibility.

What does the before-and-after evidence say to do?

The before-and-after is a change in operating model, dated to the publication of the evidence, not a claim that page quality stopped mattering overnight. Before October 10, 2026, many teams could reasonably make page-level SEO signals the center of a ChatGPT citation plan because those signals were visible and easy to audit. After the preprint, the more defensible working model is to ask whether content matches the meanings of the rewritten queries that determine retrieval. The paper still says content matters. It says content acts mainly through semantic matching to the engine's queries.

Start with a focused prompt set. Collect the commercial questions that matter to the buyer and group them by decision rather than by a spreadsheet of near-identical keywords. Inspect the answers, sources, cited URLs, absent competitors, and query wording that appears in the response. Then map each prompt to the page that should deserve retrieval. A page does not need to mirror every possible phrase. It needs to answer one decision completely enough that its title, headings, paragraphs, and evidence make the match easy to recognize.

Next, repair coverage gaps before producing more variants. If a page says it serves a category but never explains a buyer constraint, it may not match the narrow query that prompts retrieval. Add the missing explanation only when you can substantiate it. Use plain descriptions, ordered decision criteria, comparison detail, and dated sources. Do not manufacture a new FAQ merely because the study mentions FAQ blocks. The goal is useful semantic coverage, not a collection of page decorations.

Finally, measure the result over time. Publish a small set of improved pages, preserve the date and scope of each change, and watch citation outcomes across the same prompt set. This protects against a common error: declaring success after one answer changes. An answer engine can vary by prompt, time, locale, and its own underlying retrieval. Citation Engineering means building quality and coverage that can earn retrieval, then testing whether citations actually follow.

1. Before October 10, 2026: prioritize visible page-level signals as the main working model.
2. After October 10, 2026: test retrieval for rewritten queries as the leading working model.
3. What to do now: map buyer questions to complete, evidence-led pages and measure citations by prompt.
4. What not to do: stuff speculative rewritten queries or promise a citation outcome.

How should a content team respond without guessing the hidden query?

A content team should respond by making the intended answer obvious, not by pretending it can see an exact hidden query. The study gives a mechanism, but it does not provide a public list of every ChatGPT rewrite for every market. Guessing a string and repeating it is weak editorial practice. Build from questions a real buyer would ask, then write the page so a retrieval system can connect the page to the decision behind that question.

Use first-hand facts wherever possible. For a software category page, explain the job the product handles, the scope it does not handle, the buyer conditions that change fit, and the evidence behind important claims. For a service page, state the service area, service type, process, and verifiable proof. This gives an engine more than a branded assertion. It gives it a page that can is a source when an answer needs a particular detail.

Organize clusters around distinct decisions. One strong category page can establish the core definition. Supporting pages can then address comparisons, implementation questions, pricing models, industry use cases, or geographic intent. Each should link to the page that owns the broader decision and to relevant siblings. The point is not to create a giant content maze. It is to avoid a site where every page says nearly the same thing and none owns the question fully.

Keep freshness practical. Review pages when the product, market, comparison, or source changes. Update what is no longer true, add date context where it matters, and keep sources accessible. Freshness is not a cosmetic timestamp. It is the condition that lets a page remain a credible citation when an engine retrieves it. A source with dated evidence is more useful than a polished claim with no proof.

1. Write for the buyer decision, not a guessed query string.
2. State scope and limits alongside product or service claims.
3. Build distinct pages for distinct questions, then connect the cluster.
4. Record changes and recheck citation outcomes against the same prompts.

What should leaders measure after changing content?

Leaders should measure citations at the answer level and connect them to a documented page change. Start with a stable set of commercial prompts, then record the engine, date, answer, cited domains, cited URLs, and named competitors. This creates a baseline for Citation Share. Without a fixed question universe, an apparent gain can be a change in prompts rather than a change in visibility.

Measure page retrieval proxies carefully. The study observed the engines' own search behavior through API replay, a method most marketing teams cannot reproduce exactly. Teams can still form useful operational signals: whether a page is cited for the target prompt, whether a more specific sibling is cited instead, whether a competitor owns the answer, and whether the page contains the factual material the answer needs. Do not label these signals as direct access to ChatGPT's hidden query logs.

Use experiments that are small enough to explain. Change one page cluster or one decision gap, document the publication date, and compare the same prompt set over a meaningful observation window. If citations improve, the result is evidence for that market and content set. If they do not, inspect whether the page was actually eligible for the answer, whether a competitor offered better source material, or whether the answer requires a different asset type. The right response is diagnosis, not more generic output.

The study is also a warning against easy narratives. Brand knowledge affected ranking outcomes in its controlled experiments, which means a publisher can do strong page work and still face a remembered brand. That is not a reason to stop. It is a reason to report honestly. Omnicite's role is to engineer authoritative coverage and track citation outcomes across ChatGPT, Perplexity, Gemini, Copilot, and AI Overviews, not to claim that any one edit can force a model to cite a page.

1. Citation Share by category and prompt group.
2. Citation Count per day to show volume.
3. Answer Presence across the tracked question universe.
4. Share of Voice against named competitors.
5. A dated change log linking page work to later observations.

Sources

Source: arXiv, Mahe Chen and Nick Koudas

The study examined 158,099 answers and 468,037 citations, and reports 32% explained variation for ChatGPT Search retrieval versus 2.8% for measured page properties. arXiv, Mahe Chen and Nick Koudas, 2026-10-10

Source: Anything Engine Optimization

The reported findings include rewritten sub-queries, within-site citation analysis, and controlled experiments on the influence of brand names. Anything Engine Optimization, 2026-10-13