Optimizing Your Content for ChatGPT

“Optimising for ChatGPT” splits into two quite different jobs depending on how the answer was produced. If ChatGPT answered from what the model already carries, you are influencing what gets widely published about you and how consistently it is stated. If it answered by searching the web first, you are influencing what a crawler can fetch right now.

Most advice on this topic quietly assumes one or the other. This tutorial starts by finding out which one you are dealing with, because the work diverges sharply from there.

Step 1: Measure ChatGPT on its own before changing anything

Run a scan, then open AI Rankings in the Monitor group and read the ChatGPT chips specifically. For each tracked prompt you will see either a position badge, or “not mentioned”, plus the sentiment recorded for that answer.

Write down three numbers for ChatGPT alone before you touch any content:

• How many of your prompts mention you at all.
• Where you land when you are mentioned: first, top three, mid-list, or a passing reference with no position.
• Whether the sentiment is positive, neutral or negative.

These are separable problems with separate fixes, and skipping this step is how people spend a quarter rewriting product pages for what turns out to be a coverage problem.

Do this per project and per prompt set. An engine-level average across prompts you do not care about is not a baseline.

Step 2: Work out whether ChatGPT’s answers are grounded

Open Citations and filter to ChatGPT. What you find there tells you which job you are doing.

• Citations with real URLs mean the answer was produced with a live web search. The engine fetched pages and used them. Your lever is retrievability and the quality of what is retrievable.
• Citations that are bare domains with nothing to click mean the answer came from the model without searching, and the analyser extracted the domain names the answer happened to mention. Your lever is what is widely and consistently published about you, which moves far more slowly.
• No citations at all from ChatGPT usually means the same thing as the second case, with an answer that named no sources.

Search grounding is off by default for a deployment and is an operations setting rather than a per-project toggle, so if you are seeing no URLs from any engine except Perplexity, that is why. Knowing which regime you are in is worth more than any tactic in this tutorial.

Step 3: Make sure the right OpenAI agent can reach you

This is the step most often got wrong, and it is got wrong in a way that is invisible until you measure.

OpenAI documents its crawlers separately, and they are not interchangeable: one gathers material that may be used for training, one supports search results, and one fetches a page because a user’s request needs it. Blocking all of them because you object to training also removes you from the search-backed answers you are trying to appear in.

Decide per agent, deliberately, and record the decision. Then check that your robots.txt says what you think it says. The rules are matched per user-agent group with longest-match precedence under RFC 9309, so a Disallow in the wildcard group and an Allow in a named group do not combine the way people expect. A group naming a specific agent replaces the wildcard group for that agent entirely rather than adding to it.

Two more retrievability failures worth ruling out: content that only exists after client-side rendering, and pages behind interstitials or consent walls. Both look fine to a human and empty to a fetcher.

Step 4: Write pages that can be quoted, not just read

A grounded answer is assembled from passages. A page that can be quoted in one is one where the answer to a specific question is stated plainly, near the top, in a sentence that survives being lifted out of context.

That means:

• One question per page, phrased the way a person would ask it, answered in the first paragraph, not after four hundred words of preamble.
• Say what you are, who it is for, and what it is not. “We provide solutions for businesses” is unusable. “An AI visibility monitoring platform that tracks how a brand appears in ChatGPT, Claude, Gemini and other answer engines” can be lifted verbatim into an answer, because it names a category and an audience.
• Attach evidence to factual claims. Numbers, dates and comparisons that a reader (or a model) can check are what make a page usable as a source rather than as marketing.
• Keep the facts current and consistent everywhere. Inconsistency between your own pages is one of the more reliable ways to end up with a confidently wrong answer, because there is no single version to prefer.

Google’s guidance on creating helpful, reliable, people-first content describes the same standard from the search side. It transfers cleanly here: the bar is answering the question, not covering the topic.

Step 5: Earn corroboration outside your own domain

Neither regime is won on your own site alone. An ungrounded answer reflects what was widely published about you; a grounded one reflects what a search returned, which is rarely only you.

Use the Source gaps card on the Citations page as the target list. Those are the third-party domains the engines reached for on your questions. The card is cross-engine by construction and cannot be narrowed to one: each row is a domain with a count and a badge for every engine that cited it, and the search box further down the page filters the citation list below, not this card. Read the badges to see whether ChatGPT is among the engines that reached for a given domain.

Presence on those domains, through listings, comparisons, reviews or quoted contributions, is what puts you into the material the answer is built from. Prioritise domains that appear repeatedly and across several engines, not the single highest-authority name.

Step 6: Re-measure on the same prompts

Change one thing at a time, then re-scan the same prompt set. If you change prompts and content together you have changed the instrument and the subject simultaneously and can attribute nothing.

Read the position distribution rather than the score. A move from “not mentioned” to “mid-list” is a real win that barely shifts a composite score, and a score that jumped because one engine had a good day is not a win at all. Expect several scans before a change is legible: these are sampling-based models, and single-run differences are partly noise.

If nothing moves after a fair interval, go back to Step 2. Content changes cannot affect an answer that was never grounded in a fetch, and no amount of on-page work substitutes for corroboration elsewhere.

Step 7: Where to go next

• Multi-engine monitoring: ChatGPT is one of seven AI models; the others will disagree with it.
• Optimizing for ChatGPT: the reference article behind this walkthrough.
• Does ChatGPT cite websites?: what a citation from it does and does not prove.
• Writing for AI citation: how to phrase an answer so it can be lifted.
• Citation Intelligence Deep Dive: reading the source list this tutorial keeps sending you back to.