GEO by Engine: How ChatGPT, Claude, Gemini, and Perplexity Really Decide What to Say

If you've spent any time trying to figure out "AI SEO" or "GEO" this year, you've probably noticed most of the advice sounds identical no matter which platform it's supposedly for. That's a problem, because ChatGPT, Claude, Gemini, and Perplexity are not making the same decisions when they choose what to tell someone. Treating them as one target is why a lot of that advice quietly falls flat once you actually test it.

Let's be upfront about the limits here: nobody outside OpenAI, Anthropic, Google, and Perplexity has the actual formula each of these systems uses. Those systems are proprietary and they change constantly. What's possible is understanding the documented mechanics they share, where they genuinely diverge, and what that means for how you write and structure content.

One shared recipe, four different jobs

Every one of these tools goes through a similar build process: a massive pretraining stage on text, instruction tuning so the model follows directions, and preference optimization — usually reinforcement learning from human feedback — where human reviewers judge which of two answers is better. That last stage is where a model's personality gets set: how cautious it is, how much it explains, how it structures a response by default.

From that shared foundation, each company aimed their model somewhere different. Perplexity built its product around live retrieval, so its answers read more like a research brief than a chat — citation-heavy and pulled from whatever's currently on the web. Claude was tuned for depth: holding long context, reasoning through multi-step problems carefully, and it tends to score well on calibration — meaning how often its confidence actually matches whether it's right. ChatGPT covers the broadest range of everyday tasks and carries the largest user base of the four, though that flexibility also means output quality can swing more without a detailed prompt. Gemini's real advantage shows up the moment a task touches Google's own ecosystem — Docs, Sheets, Gmail, YouTube — where it can work with data the other three simply can't see.

What's actually happening when a model picks an answer

There are really three layers at work here.

Training-time shaping happens once, ahead of time — this is the pretraining and preference-tuning stage that sets a model's defaults.

Inference-time selection happens every time you send a prompt, as the model weighs candidate responses against what it learned during training.

Product-time retrieval is the layer that varies most visibly between platforms — whether the tool pulls in outside information at the moment you ask (retrieval-augmented generation, or RAG) or relies mostly on patterns already baked into its training. This is a big part of why some of these tools feel more like a search engine and others feel more like a conversation with someone who already knows a lot.

There's actual public data behind that preference-tuning stage, and it's worth mentioning because it makes the concept concrete instead of abstract. Anthropic published a dataset called hh-rlhf in 2022, made up of 169,000 rows where a human grader picked which of two responses they preferred. Nvidia followed in 2024 with HelpSteer2, which grades responses across five separate categories instead of one: helpfulness, correctness, coherence, complexity, and verbosity. An independent review of both files found that under that newer five-category rubric, answers formatted as lists tended to score higher more often than not - which is a plausible, if unconfirmed, explanation for why so many AI answers default to bullet points. Worth flagging: neither dataset reflects how these labs train models today. They're historical snapshots, not a live map of current ranking logic.

Where each engine actually pulls ahead

Perplexity wins on citation accuracy and freshness, since it's grounded in the live web at the moment you ask rather than leaning on older training data. It's noticeably weaker on anything creative or long-form — ask it to draft a full article and it reads more functional than polished.

Claude tends to lead on tasks that require holding a lot of context and reasoning carefully through it, and it's repeatedly ranked strong on calibration — not overstating confidence on claims where being wrong actually costs something. That's a big part of why it gets used for long-form writing and multi-step agent work rather than quick lookups.

ChatGPT has the widest feature set and the biggest install base — voice, vision, browsing, image generation, all layered onto a general-purpose core. That breadth is genuinely useful, but testers consistently note more variance in output quality without a well-specified prompt.

Gemini's edge isn't really about raw model quality — it's about what it can see. The moment a task touches your Google Workspace data, Gemini can act on it directly instead of talking about it in the abstract.

None of the four wins outright, and that's worth sitting with rather than resolving. Most credible testing in this space lands on some version of "use the right tool for the task," and standings shift often enough that any single comparison should be treated as a snapshot, not a permanent ranking.

What this changes about how you write

The tactical differences follow directly from what each engine rewards:

• For Perplexity, lead with sources — original data, cited statistics, claims that are clearly attributable. Write in self-contained statements a retrieval system could lift out cleanly without needing the paragraph around it.
• For Claude, lead with structure and a real point of view. Long-form content with a clear argument and genuine first-hand experience behind it earns more weight here than a shallow overview covering the same ground.
• For ChatGPT, write content that works both as a full explainer and as smaller, standalone pieces, since a large share of its usage comes through follow-up questions rather than a single query.
• For Gemini, invest in clean entity definitions and structured data — schema markup that helps Google's own systems understand exactly what your content is about, since that's the layer it's actually reading from.

One more distinction worth remembering: these engines don't weight sources the same way. Some lean more on a brand's own site. Others lean more on third-party mentions and citations the brand doesn't control. Knowing which lever matters for a given engine changes where you should actually spend your time.

How to know if it's working

Traditional rank tracking doesn't map cleanly onto this. A more useful set to watch: how often your content actually gets cited (not just mentioned), whether you're landing inside the direct answer or getting pushed to a secondary link, how many distinct sources a given engine pulls from on topics you care about, and — especially for retrieval-heavy tools like Perplexity — how recently your content was updated, since freshness can be the difference between getting pulled into an answer at all.

None of this replaces the metrics you already track. It sits alongside them, and together they tell you whether you're actually showing up where people are asking.

Where Eaglemount Technologies Fits In

Understanding how these engines work is one thing — actually showing up in them is another. At Eaglemount Technologies, we build the full picture: websites structured the way AI systems can actually read them, SEO that accounts for both Google and AI search, and content strategy that gives ChatGPT, Claude, Gemini, and Perplexity something real to cite you for — not generic copy that gets lost in the noise.

We've helped businesses across Australia, Asia, Europe, and North America get found in markets that don't all search the same way, and AI visibility is no different — what works for one platform, one region, or one audience doesn't automatically work for another.

Not sure if your business would even come up in an AI-generated answer right now? That's exactly what we can find out for you.

Get Your Free Strategy Consultation — let's talk about what it actually takes to get you found, everywhere your customers are asking.

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