AI & Answer Engines
How to Be Represented Accurately by AI Search
ChatGPT, Perplexity and Google's AI already describe people. Here is how to make sure they get you right.
When someone asks ChatGPT, Perplexity or Google's AI about you, the answer is assembled from whatever those systems can find and trust across the web. If the material about you is thin, inconsistent or missing, the answer will be too - and it may be confidently wrong. Being represented accurately is largely a matter of giving these systems clear, consistent, well-structured material to draw on.
This is sometimes called generative engine optimisation, or GEO. Stripped of the jargon, it is the same discipline as being quotable: say who you are plainly, in a form a machine can lift without guessing, in enough places that the machine treats it as settled fact.
How AI engines decide what to say about a person
Two mechanisms are at work. Some knowledge is baked into the model from its training data. Increasingly, though, these tools also retrieve live pages at the moment you ask and summarise them. In both cases the system is looking for a consistent signal across multiple sources - a consensus about your name, role and work. Where sources agree, it answers confidently. Where they conflict or fall silent, it either hedges, omits you, or fills the gap with a plausible guess.
Why AI gets people wrong
The usual causes are mundane. A thin footprint gives the model almost nothing to work with. Conflicting bios - three different titles, two different employers - give it no settled version to report. A shared name merges you with someone else. And an absent authoritative source means the model leans on whatever low-quality page it can find. None of these are malicious; they are gaps, and gaps are fixable.
Entity clarity: one consistent identity
The single most useful thing you can do is make yourself an unambiguous entity. In practice that means the same name, current role and short description wherever you appear; linking your own profiles to each other so a machine can see they are the same person; and, on any page you control, using structured data - Person markup with your name, role and the profiles that are also you - so the facts are machine-readable rather than merely implied. Consistency is what turns scattered mentions into a confident answer.
Why a question-and-answer format is easy for AI to quote
Large language models reward content that states a clear answer directly, under a heading that matches the question being asked, with specific details attached. A question-and-answer page is almost purpose-built for this: each question is a heading, each answer is a self-contained, quotable passage. It is far easier for a model to lift "here is how this person defines success" from a labelled answer than to infer it from a paragraph of prose. This is a large part of why structured interviews travel well into AI summaries.
Owned and independent sources both matter
Pages you control establish the canonical facts. Independent mentions - interviews, coverage, profiles on reputable sites - corroborate them, and corroboration is what moves a claim from "asserted" to "trusted". You want both: a clear account in your own words, and at least a few credible places elsewhere that agree with it.
What you can do this month
- Audit the answer. Ask ChatGPT, Perplexity and Google's AI "who is [your name]" and note what is wrong, missing or merged with someone else.
- Align your bios. Make your name, role and description identical across your profiles, and link them to each other.
- Publish one authoritative first-person account on a credible site, structured so each point is stated plainly under a clear heading.
- Add structured data on pages you control, so the facts are machine-readable.
- Check crawler access. Make sure your robots.txt does not block AI crawlers like GPTBot and ClaudeBot unless you intend to.
A published Q&A profile delivers several of these at once: a first-person account, in a question-led structure models can quote, carrying structured data and linking to your other profiles. See also what people find when they search your name.
Common questions
How does ChatGPT decide what to say about me?
It combines what it learned in training with pages it can retrieve at the time of the question, and looks for a consistent signal across sources. Where your name, role and work are described consistently in several credible places, it answers confidently; where sources are thin or conflicting, it hedges, omits you, or guesses.
Why does AI get my details wrong?
Usually because of gaps rather than malice: a thin online footprint, conflicting bios across profiles, a shared name that merges you with someone else, or the absence of an authoritative source. Each of these is fixable by publishing clear, consistent material and aligning your profiles.
What is generative engine optimisation (GEO)?
GEO is the practice of making your content easy for AI answer engines to find, trust and quote - through entity clarity, consistent descriptions, structured data, a direct question-and-answer style, and corroboration from independent sources. For a person, it means being an unambiguous, well-documented entity.
Does structured data really help AI understand me?
It helps by making facts explicit rather than implied. Person markup that states your name, role and linked profiles gives machines an unambiguous record to read, which reduces the chance of confusion with someone else and supports a more accurate summary.
The quickest way to give answer engines something accurate to work with is to publish one clear, first-person account and point your other profiles at it. A reviewed Q&A profile does precisely that, structured so a machine can quote it without guessing. See how published interviews read, or start your own.