Every AI engine reads from a different place before it writes. Perplexity searches the live web. Google AI Overviews and Gemini lean on Google Search. ChatGPT and Claude answer from a trained model and browse when asked. Grok pulls from posts on X. This is the per-engine tactics piece, not the general guide to how an answer gets built. It tells you what each engine retrieves, what it rewards, and the one lever that moves it.
Perplexity: the live-search engine
Perplexity runs a fresh web search for almost every question. It reads the top results, writes a short answer, and attaches numbered citations that link to the exact pages it used. If your page is not in the set it retrieved, you are not in the answer.
It rewards pages it can fetch and quote right now. That means clean HTML, a direct answer near the top, clear headings, and content recent enough to trust. Because the citation is visible, one quotable sentence on your page can become the line Perplexity lifts.
The lever here is retrievability. Serve the page so the text is present without running JavaScript, state the answer plainly before the supporting detail, and keep the facts current. Perplexity is the engine where a clean, fresh, well-structured page gets named fastest.
Google AI Overviews and Gemini: the Google-grounded pair
Google AI Overviews are written from Google Search results. Gemini grounds its answers in Google Search as well. Both start from what Google has already indexed and understood about you, so your ordinary search visibility carries straight into the AI answer.
This is the cluster where classic SEO still does the work. Pages that are indexed, relevant, and technically sound feed the summary. Structured data helps Google read your facts without guessing. Strong entity signals, a consistent brand name, a clear description of what you do, and corroboration across the web, help both engines know who you are and what to say about you.
The lever is Google Search itself. If you rank and Google understands your entity, you are in the pool these answers draw from. Add structured data for the facts you want quoted, and keep your name and category consistent everywhere they appear.
ChatGPT and Claude: trained model, live browsing
ChatGPT and Claude answer first from a trained model, a large body of text they learned before the conversation began. ChatGPT can also browse the live web, backed by Bing, when search is on or the question needs current facts. Claude uses web search when that tool is enabled. So you are optimizing two layers at once.
The training layer rewards durable presence. When your brand is described consistently across many reputable sources over time, the model is more likely to have learned it and to name you without being prompted. The browse layer rewards the same things Perplexity does: a current, retrievable page an engine can read on demand. Both lean toward sources they treat as authoritative.
The lever is authority that compounds. You cannot edit the training data, but you can earn the wide, consistent, credible coverage that shapes the next version of it, and you can keep a clean current footprint for the moments these engines browse. Plan in years, not a single publish.
Grok, and what wins on every engine
Grok leans on real-time posts from X alongside web results. That makes timeliness and live conversation count for more here than on the other engines. A topic being actively discussed, with your brand inside that discussion, feeds directly into what Grok says.
The lever is presence in the current conversation. Earned mentions, community threads, and recent posts about your category matter more on Grok than a static page does. This is the engine most sensitive to what people are saying right now.
Read across all five and the strategy converges. Strong, accurate, well-sourced content that directly answers the question wins on every engine. The differences are emphasis: Perplexity rewards a retrievable page, the Google pair rewards search visibility and entity clarity, the trained-model pair rewards durable authority, and Grok rewards timely discussion. The per-engine notes tell you where to push next, not which engine needs a separate playbook.