ChatGPT vs Perplexity vs Google AI Overviews — what's actually different

AI search isn’t one thing. ChatGPT, Perplexity, and Google’s AI Overviews work differently under the hood and cite content for different reasons. Understanding the differences helps you optimize for all three without chasing one at the expense of the others.

The three systems at a glance

• ChatGPT — OpenAI’s general-purpose chat assistant, with web browsing and search capabilities.
• Perplexity — A search-first AI, built from the ground up to cite sources in every answer.
• Google AI Overviews — Google’s generative answer layer above traditional search results.

All three can cite your website. All three use somewhat different signals to decide whether to.

ChatGPT

ChatGPT answers from a combination of its training data (older) and live web browsing (current). For local-business queries, it leans heavily on the live web.

What it weights:

• Clean, crawlable HTML.
• Authoritative third-party mentions (press, directories, Wikipedia-adjacent).
• Well-structured content with clear headings.
• Schema markup it can parse.

What to do: Write pages with clean HTML, publish llms.txt, make sure GPTBot isn’t blocked in robots.txt.

Perplexity

Perplexity is explicitly built for search-with-citations. Every answer includes numbered sources. It’s the most “honest” AI search system in terms of citing its sources.

What it weights:

• Freshness and recency heavily.
• Content structured as clear answers to clear questions.
• Sites that publish frequently and update old content.
• FAQ-style structured content.

What to do: Publish regularly. Keep content dated. Add FAQPage schema aggressively. Make sure PerplexityBot is allowed.

Google AI Overviews

Google’s AI-generated summary that appears above traditional search results. It pulls from the broader Google index, with additional weight on authoritative sources.

What it weights:

• Traditional SEO authority signals (backlinks, E-E-A-T, established domain history).
• Schema markup extensively.
• Content that’s unambiguously on-topic.
• Local signals, when location is implied.

What to do: Traditional SEO still matters here most. Add every schema type you can. Write pages that directly answer specific questions.

The overlap

Three things all three systems care about:

1. Clear content structure — headings, short paragraphs, lists, tables.
2. Schema markup — the machine-readable summary of each page.
3. Unambiguous, quotable text — passages that can be lifted as direct answers.

If you optimize for these three, you’re 80% of the way to showing up in all three systems.

What to stop doing

• Writing long, keyword-stuffed pages. All three AI systems see through it now.
• Blocking AI crawlers by default. You can’t be cited if you can’t be read.
• Treating AI search as a separate project from SEO. Most of the foundational work overlaps.

How to optimize for all three at once

• Clean, semantic HTML.
• Schema on every page (LocalBusiness, Service, FAQPage, BlogPosting, BreadcrumbList).
• llms.txt at root, maintained quarterly.
• Allow GPTBot, ClaudeBot, PerplexityBot, and Google-Extended in robots.txt.
• Content written as clear answers to specific questions.
• Publish regularly. Update old content. Date everything.

Do all of that and you’ll be cited across the major AI search systems. Skip it and you won’t be part of the conversation when your buyers go looking.

Want help setting this up? That’s what AI Search Optimization does.