Why Do AI Engines Give Different Answers to the Same Question?

Why do AI engines give different answers to the same question?

Because they are not doing the same thing. Each engine runs its own retrieval, over its own index, built by its own crawler, then hands the results to a different model. Google says this plainly about its own two features, and if two features inside one company diverge, two companies will diverge further.

People ask me this constantly, usually in a frustrated tone. They check a question in ChatGPT, then in Perplexity, then in Google, and get three different sets of sources. Then they ask which one is right, or which one to optimise for.

Both questions have the same answer, and it is not the one people want. The variation is structural. It is not a bug you can tune away, and understanding where it comes from tells you exactly which work is worth doing and which work is superstition.

What is query fan-out, and why does it scatter results?

Query fan-out is when an engine turns your one question into many searches. Google's documentation describes it directly, saying that both AI Overviews and AI Mode may use a query fan-out technique, issuing multiple related searches across subtopics and data sources, to develop a response.

Follow what that means for your page. You are no longer competing for the question a person typed. You are competing for a set of sub-questions the engine invented on the reader's behalf, and you have no visibility into that set. Two engines that phrase those sub-questions differently will surface different pages, even from an identical index.

Google is explicit that this widens the field rather than narrowing it. Its documentation says that while responses are being generated, its models identify more supporting web pages, allowing a wider and more diverse set of helpful links than with a classic web search. That is good news for smaller sites, and it is also the mechanism that makes results feel unpredictable.

The practical consequence is a shift in what you should write. A page built to answer one keyword has one way in. A page that genuinely covers the sub-questions around a topic has many. That is not a trick, it is just what fan-out rewards.

Do different engines even use the same robot?

No, and this is the part site owners most often miss. Each company runs its own crawler with its own name, and access is granted or denied per crawler in your robots file. Two engines can hold genuinely different pictures of your site because you told them different things.

OpenAI's documentation says OpenAI uses OAI-SearchBot and GPTBot, and that OAI-SearchBot is used to surface websites in search results in ChatGPT's search features. It also states that sites opted out of OAI-SearchBot will not be shown in ChatGPT search answers, though they can still appear as navigational links. Separately, it says OpenAI uses ChatGPT-User for certain user actions in ChatGPT and Custom GPTs.

Perplexity's documentation describes a matching split. It says PerplexityBot is designed to surface and link websites in search results on Perplexity, and recommends allowing PerplexityBot in your robots file so your site appears in those results. It says Perplexity-User supports user actions within Perplexity and controls which sites those user requests can access, and it publishes IP address endpoints for both.

Google's approach is different again. Its documentation says AI is built into Search and integral to how Search functions, which is why robots directives for Googlebot are the control for site owners. One crawler, one control, covering both classic results and AI features.

What happens if your robots file treats those robots differently?

You get different answers, by your own instruction. This is the most common self-inflicted cause of divergence I find when I audit a site, and it is almost always accidental rather than deliberate.

OpenAI's documentation makes the independence explicit, noting that each setting is independent of the others, so a webmaster can allow OAI-SearchBot to appear in search results while disallowing GPTBot to indicate that crawled content should not be used for training its generative AI foundation models. That is a genuinely useful distinction. Appearing in an answer and being used as training data are separate decisions, and the robots file is where you make them separately.

Where it goes wrong is copy and paste. Someone finds a blocklist of AI crawlers, pastes it in to stop training use, and unknowingly blocks the search crawlers too. Months later they wonder why one engine never mentions them. The file is doing exactly what it was told.

So the first diagnostic is not content. It is your robots file, read line by line, with each user agent checked against what that company's own documentation says the agent does. Ten minutes of reading resolves more mysteries than a month of rewriting pages.

Does one engine's answer predict another's?

Not reliably, and Google says as much about its own products. Its documentation states that AI Mode and AI Overviews may use different models and techniques, so the set of responses and links they show will vary. If that holds between two features of one search engine, treating any engine as a proxy for another is guesswork.

There is a further wrinkle specific to Google. Its documentation says AI Overviews are only shown when its systems determine that the feature is additive to classic Search, and as such often do not trigger. So an absent AI Overview is not evidence that you failed to qualify for one. It may simply mean the feature did not appear at all for that query.

This is why I distrust the screenshot as a measurement instrument. A single check on a single day, on one account, in one location, tells you almost nothing. It is a sample of one from a system that is explicitly documented as varying.

What I do instead is look for patterns across many checks and many phrasings, and I treat a single bad result as noise until it repeats. That discipline is unglamorous, and it prevents a lot of expensive overreaction. I have argued the same thing about whether publishing frequency affects AI citations, where the temptation to read a trend into two data points is very strong.

Should you optimise separately for each engine?

Mostly no, and Google's guidance is unusually blunt about this. Its documentation says there are no additional requirements to appear in AI Overviews or AI Mode, nor other special optimizations necessary, and that the existing SEO fundamentals continue to be worthwhile.

It goes further on the question of special files and markup. Google's documentation says you do not need to create new machine readable files, AI text files, or markup to appear in these features, and that there is no special schema.org structured data you need to add. That sentence has saved several of my clients a pointless project.

On eligibility it is equally specific. To be shown as a supporting link in AI Overviews or AI Mode, a page must be indexed and eligible to appear in Google Search with a snippet, fulfilling the Search technical requirements, with no additional technical requirements. And it warns that meeting every requirement does not guarantee that Google will crawl, index, or serve the content, because indexing and serving are not guaranteed.

The one place per-engine work is genuinely real is access. Crawler permissions are per-company and must be set per-company. Everything downstream of access is shared: being indexed, being readable as text, being clearly organised, being trustworthy. After 350 published articles on answer engines and schema, that is the split I keep landing on. Access is specific. Quality is universal.

How do you measure something that changes every time you ask?

You stop measuring instances and start measuring rates. One answer is an anecdote. The share of checks in which you appear, across many phrasings over weeks, is data you can act on.

For Google specifically there is a real reporting path. Its documentation says sites appearing in AI features are included in overall search traffic in Search Console, reported in the Performance report within the Web search type. So the traffic is not invisible, it is blended, which means you are looking for shifts in the mix rather than a separate line item.

Google also makes a claim worth quoting carefully because it is their observation rather than an independent finding. Its documentation says that when people click from search results pages with AI Overviews, those clicks are higher quality, meaning users are more likely to spend more time on the site. Take that as the vendor's own reported observation, and check it against your own time-on-page rather than assuming it.

Beyond Google, honest measurement is harder, and I would rather say that than pretend otherwise. Build your own repeatable check: a fixed list of questions, run on a schedule, recorded in a sheet, with the date and the engine. It is manual and it is unsexy, and it is the only thing I have found that survives contact with how much these systems vary. The approach I described for reading AI citation data in Search Console pairs well with it.

What actually moves the needle across all of them?

The boring fundamentals, applied properly. Google's own list of what continues to be worthwhile includes ensuring crawling is allowed in robots.txt and by any CDN or hosting infrastructure, making content findable through internal links, providing a good page experience, making sure important content is available in textual form, and making structured data match the visible text on the page.

Read that list again with fan-out in mind and it gets more interesting. Internal links matter more when an engine is exploring subtopics, because they are how it finds the neighbouring page that answers the sub-question. Textual content matters more when a machine is reading rather than a person skimming. Structured data matching visible text matters more when trust is being assessed by something that can compare the two.

My own bias, and I will label it as opinion rather than fact, is that the highest-leverage work in 2026 is coverage plus clarity. Cover the questions around your topic properly, then make each answer easy to lift out of the page in isolation. That serves fan-out, serves a reader skimming, and serves an engine that needs a self-contained passage to quote. It is also just good writing, which is why I trust it.

What should you do next?

Start with your robots file. Open it, list every AI user agent it mentions, and check each one against that company's own documentation rather than a blog post. Fix any crawler you are blocking by accident. That single pass explains most cases of an engine ignoring a site, and it is the same audit I run when checking which sources actually get cited in AI Overviews.

Then stop comparing engines and start tracking one of them properly over time. Pick your ten most important questions, decide how often you will check them, and write the results down with dates. Variation stops looking like chaos once you have enough rows to see the shape of it.

If you want help reading your own crawler access, or you want someone to build that measurement loop with you rather than hand you another dashboard, reach out. It is most of what I do, and the first hour usually finds something worth fixing.