Optimising for Each AI Platform: ChatGPT vs. Perplexity vs. Gemini
ChatGPT, Perplexity, Gemini and Copilot each crawl differently. How they differ, and one unified strategy that covers all four.
"Optimise for AI" sounds like one task. It isn't. Each AI engine crawls differently, weights different signals, and surfaces content in its own way. A site can be perfectly visible to Gemini and nearly invisible to ChatGPT — that exact split shows up in real audits. Here's how the major platforms differ and how to design for each.
The platform cheat sheet
| Platform | Renders JS? | Leans on | Your priority |
|---|---|---|---|
| Google AI Overviews / Gemini | Yes (Googlebot) | Search index, schema, freshness | Strong SEO + schema |
| ChatGPT (GPTBot/search) | No | Server HTML, training data, llms.txt | SSR + llms.txt |
| Perplexity | Often no | Live retrieval, clear sources | SSR + citable, sourced content |
| Bing Copilot | Partial | Bing index, schema | Bing Webmaster + schema |
Google AI Overviews & Gemini
The most forgiving. Googlebot renders JavaScript, so even client-side sites are usually visible. Wins come from doing classic things well:
- Solid schema (
FAQPage,Article,Product). - Content structured for featured-snippet extraction (definitions, lists, tables).
- Freshness signals (
dateModified). - If you already rank in Google, you're likely in the AI Overviews pool.
ChatGPT
The least forgiving for modern SPAs. GPTBot doesn't render JavaScript, so:
- SSR/SSG is mandatory — see why client-side rendering kills AI visibility.
- A strong
llms.txtis a direct line to ChatGPT — sometimes the main content it can read. - Brand presence in training data (mentions) shapes recognition.
If you optimise for one platform's quirks, make it this one — its constraints are the strictest.
Perplexity
The citation-first engine. Perplexity shows its sources prominently, so it rewards content that's:
- Server-rendered (its fetch mode often skips JS).
- Clearly sourced and factual — visible authors and dates.
- Quotable — clean passages it can attribute.
Perplexity is arguably the best place to earn visible credit, so citability and authorship pay off most here.
Bing Copilot
Powered by Bing's index. Often overlooked:
- Submit to Bing Webmaster Tools (separate from Google).
- Schema helps here too.
- Decent Bing rankings → Copilot visibility.
A unified strategy that covers all four
You don't optimise four times. Do these and you cover every engine:
- Server-render your content → unlocks ChatGPT + Perplexity, keeps Google/Bing happy.
- Ship page-specific schema → helps Gemini, Copilot, and all rich extraction.
- Maintain a strong
llms.txt→ direct value for ChatGPT/Perplexity. - Write citable, sourced, dated content → wins Perplexity, helps everywhere.
- Build brand mentions → improves recognition across all training-data-driven models.
The mindset shift
Think of the AI engines like browsers in the old cross-browser-testing days: same core content, different rendering quirks. Build to the strictest one (ChatGPT's no-JS crawler), and the rest fall into place.
Next up: the full technical checklist to run before you call a build GEO-ready — the technical GEO checklist.