Should I optimize my content differently for ChatGPT, Gemini and Perplexity?
Mostly one strategy, with per-engine checks: what Google says about Gemini-powered AI features, what research found, and how to test each engine with real data.
The short answer is that you should build one good page and then test it on each engine, rather than writing three versions. The longer answer is that the engines differ in how they find pages, which crawlers they use and which sources they favor, and you should measure those differences instead of trusting folklore. Plenty of advice online asserts that Perplexity wants one thing and ChatGPT wants another. Very little of it comes with data.
What the official guidance actually says
Google's documentation on AI features in Search is unusually direct. It says the best practices for SEO remain relevant for AI Overviews and AI Mode, that there are no additional requirements to appear in them, and that no special optimizations are necessary. A page needs to be indexed and eligible to show with a snippet. Google adds that you do not need new machine-readable files, AI text files or special schema.org markup to appear. It lists the familiar basics: allow crawling in robots.txt and through any CDN, make content findable through internal links, give users a good page experience, keep important content in text, and make sure structured data matches visible text.
The same page notes that both AI Overviews and AI Mode may use a "query fan-out" technique, issuing multiple related searches across subtopics to build a response, and that the two may use different models and techniques, so the links they show vary.
So for the Google side, including Gemini-powered search features, the official line is: do good SEO, stay crawlable, answer the question clearly. That is one strategy, not a separate one.
What research suggests about content itself
The most cited academic work on this is the 2024 paper "GEO: Generative Engine Optimization" (Aggarwal and colleagues, presented at KDD 2024). Testing on a benchmark of 10,000 queries, the authors found that adding citations, quotations from relevant sources and statistics raised visibility in generated answers by up to about 40% on their metrics, and that gains of up to 37% showed up when they tried it on Perplexity. They also found that the effect of each method varied by domain. Two lessons follow: substantive, well-sourced content tends to help, and what helps differs by topic, so you need to test in your own category.
Where the engines do differ
You do not write different prose for each engine. You manage different access and different results:
- Crawler access. Each engine fetches through different bots. ChatGPT-related bots include GPTBot, OAI-SearchBot and ChatGPT-User. Perplexity uses PerplexityBot and Perplexity-User. Google's own controls run through Googlebot, per its documentation, and crawler logs also pick up Google-Agent and GoogleOther. Blocking the wrong one by accident is a common reason a page is invisible to a single engine.
- Which sources get cited. The same prompt can cite different pages in different engines. That is a measurable fact, not a theory.
- How the engine searches. Fan-out queries differ by model, so the pages retrieved differ too.
- Which of your pages are chosen. One engine may prefer your documentation, another your blog.
The workflow: one page, three checks
- Write the page for the reader, with sourced facts, clear headings and a direct answer near the top.
- Confirm every relevant bot can fetch it, in robots.txt and at the CDN.
- Track the same prompts in each engine and compare which pages and domains are cited.
- Where one engine ignores you, look at what it does cite and why, then adjust.
- Re-measure after changes.
Tools that help you see the differences
Promptwatch fits this workflow well because it splits results by model. Prompt panels run daily per model, country and language, and dashboard templates include an AI model breakdown, with a heatmap that compares any two dimensions such as models against topics. Citation analytics shows which pages and domains each engine cites, and query fan-outs per model show how each one searches. Agent Analytics logs IP-verified AI crawler visits by bot, including GPTBot, OAI-SearchBot, ChatGPT-User, PerplexityBot and Google-Agent, with crawl-to-citation paths per page. Models tracked in real interfaces include ChatGPT search, Google AI Overviews, Google AI Mode, Gemini, Perplexity, Copilot and Meta AI. Pricing: free Explore (10 prompts, ChatGPT only), Essential $95 a month, Professional $245 (crawler logs start here), Business $579.
Alternatives: AthenaHQ covers 11 models from $295 a month, Scrunch AI tracks four LLMs from $250 a month with bot traffic reporting, and Profound covers up to nine engines on custom Enterprise pricing. Neither of the first two documents a crawler-log product at the depth above.
What not to do
Do not split your content into engine-specific versions on the strength of a blog post. Do not add markup because someone claims it wins one engine. Google says special schema is not needed for its AI features, and you should test any other claim on your own data before acting on it.
Bottom line
Optimize once, for readers, with solid facts. Then measure per engine, fix access problems, and adjust where your own citation data shows a pattern. If the data says an engine ignores a type of page, that is worth acting on. If it is only opinion, skip it.