GuideAI SEO

AI SEO Checklist: How to Optimize for ChatGPT, Perplexity, and Google AI Overviews (2025)

Search is splitting into two tracks. Google's traditional algorithm still sends traffic — but a growing share of queries now go through AI assistants that crawl, rank, and cite pages very differently. This checklist covers both.

Updated May 2025·12 min read

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What is AI SEO?

AI SEO is the practice of optimizing web content so it gets discovered, retrieved, and cited by AI-powered systems — ChatGPT, Perplexity, Claude, Google Gemini, and Apple Intelligence.

Traditional SEO optimizes for Google's ranking algorithm: backlinks, keywords, page speed, structured data. AI SEO adds a second layer: making content structurally clear enough for language models to extract, summarize, and attribute correctly.

The key difference is that traditional search shows users a list of links. AI search reads those pages, extracts the relevant facts, and gives the user a direct answer — with citations. If your page isn't easy for AI to parse and attribute, it won't get cited even if it ranks #1 in Google.

The complete AI SEO checklist

1. AI Crawler Access

Before any optimization matters, AI crawlers must be allowed to access your pages.

✓
GPTBot allowed in robots.txt
OpenAI's crawler for ChatGPT and SearchGPT. Block it and ChatGPT won't cite your pages.
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ClaudeBot allowed in robots.txt
Anthropic's crawler for Claude.ai web browsing and Claude's knowledge updates.
✓
PerplexityBot allowed in robots.txt
Perplexity's crawler for Perplexity.ai answers. Blocking it removes you from Perplexity citations.
✓
GoogleBot allowed
Still required for Google AI Overviews, which are built on top of Google's traditional index.

2. AI Content Discovery

Help AI systems find and understand what your site contains.

✓
llms.txt file present
Place a Markdown index of your best content at /llms.txt. This is the AI-native equivalent of a sitemap.
✓
XML sitemap up to date
AI crawlers also use sitemaps to discover content. Keep it current and submit to Google Search Console.
✓
RSS or Atom feed available
AI systems use feeds to detect new content. If you publish articles or updates, a feed increases citation freshness.

3. Structured Data (JSON-LD)

Structured data is how you communicate page metadata to both Google and AI systems without them having to infer it from prose.

✓
Article schema with author name
AI assistants require a named author to attribute factual claims. Anonymous content gets lower citation confidence.
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Publication and modification dates
AI systems prefer recent, dateable content. Missing dates make it harder for AI to assess freshness.
✓
FAQPage schema for Q&A content
FAQPage structured data directly feeds into Google's FAQ rich results and helps AI assistants extract Q&A pairs.
✓
Organization or Person schema
Establishes who is behind the content — a key E-E-A-T signal for both Google and AI trust scoring.
✓
BreadcrumbList schema
Helps both Google and AI systems understand your site structure and the hierarchy of the current page.

4. Content Structure for AI Extraction

AI assistants extract facts from your HTML. Content that is structured clearly gets cited more accurately.

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Summary or TL;DR in first 150 words
AI systems prioritize content near the top of the page. A clear summary dramatically increases citation probability.
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Question-phrased headings (H2/H3)
Headings like 'How does X work?' directly match search queries AI users ask. They also help AI extract the answer below.
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Lists for enumerations (not just prose)
Bulleted and numbered lists are much easier for AI to extract as discrete facts than buried prose.
✓
Outbound citations to primary sources
Content with citations is treated as more authoritative by AI systems. Link to your data sources.
✓
Content readable without JavaScript
Most AI crawlers fetch raw HTML without executing JavaScript. Pages that require JS to render key content won't be fully indexed.

5. E-E-A-T Signals

Google's E-E-A-T (Experience, Expertise, Authoritativeness, Trustworthiness) framework increasingly influences both traditional search rankings and AI citation confidence.

✓
About page with author credentials
Establish who wrote the content and why they're qualified. AI systems use author signals to weight citation confidence.
✓
Contact information accessible
A discoverable contact page is a trust signal for both Google's quality raters and AI citation engines.
✓
Privacy policy and terms present
Legal pages signal legitimate operation and are checked by quality raters evaluating YMYL content.

6. Traditional SEO Foundations

AI SEO doesn't replace traditional SEO — both are required. Google still drives the majority of web traffic, and Google's index feeds its AI Overviews.

✓
Title tag: 50–60 characters with primary keyword
Still the strongest on-page ranking signal for Google. Include your target keyword naturally.
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Meta description: 150–160 characters
Doesn't directly affect ranking but drives click-through rate from search results.
✓
Canonical URL declared
Prevents duplicate content confusion when pages are accessible at multiple URLs.
✓
HTTPS
A confirmed Google ranking signal. All pages should be served over HTTPS.
✓
Word count above 300 words
Very short pages lack sufficient content for Google to understand topical relevance.
✓
Proper H1–H6 hierarchy
One H1 per page, followed by nested subheadings. This is a structural quality signal.
✓
Image alt text on all images
Required for accessibility and image search indexing. Google penalizes missing alt text on content images.
✓
Internal linking
Links between your own pages distribute PageRank and help Google discover all your content.

The biggest mistake in AI SEO

The most common mistake is treating AI SEO as a separate checklist to complete after traditional SEO. The two are deeply interconnected. A page that ranks well in Google is more likely to be in the training data or retrieval pool of AI systems. A page optimized for AI citation (structured, authoritative, well-sourced) also tends to rank better in Google.

The single highest-leverage action most sites can take is adding a named author with credentials to all content, combined with an llms.txt file. These two changes together address the most common AI citation failure mode: content that exists but isn't trusted enough to cite.

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