How to Make Your Website LLM-Friendly (Technical Checklist for AI Crawlers)

What “LLM-friendly” really means

Large language models reach your content in two ways: through training data collected by crawlers, and through live retrieval when a chat product searches the web to answer a question (ChatGPT search, Perplexity, Claude with web search, Google AI Overviews). Being LLM-friendly means making your pages easy to fetch, parse, trust and quote in both situations.

No technique can guarantee a citation. What you can do is remove every technical barrier and give models clear, verifiable, well-structured content that is worth quoting.

The 10-point technical checklist

1. Allow the right crawlers. Check robots.txt and your CDN/firewall (Cloudflare bot rules often block AI bots by default). Decide deliberately about search-retrieval bots (OAI-SearchBot, ChatGPT-User, Claude-SearchBot, Claude-User, PerplexityBot, Perplexity-User) versus training bots (GPTBot, ClaudeBot, Google-Extended, Applebot-Extended, CCBot).
2. Server-render your content. If the text only appears after JavaScript runs, many AI crawlers will see an empty page. Use static generation or SSR for pages you want cited.
3. Use semantic HTML. One H1, logical H2/H3 hierarchy, real lists and tables, descriptive link text. Models extract meaning from structure.
4. Add JSON-LD schema. Organization, WebSite, Service/Product, Article, FAQPage, HowTo and BreadcrumbList. Keep it consistent with visible text.
5. Publish an XML sitemap with accurate lastmod dates and submit it to Google Search Console and Bing Webmaster Tools. ChatGPT search relies in part on Bing-indexed content, so Bing indexing matters.
6. Add an llms.txt file at /llms.txt: a short Markdown index of your most important pages. It is a proposed convention, not a standard. Google has said Google Search does not use llms.txt, and no major AI vendor has formally confirmed it relies on it, so treat it as a low-cost convenience for developer tools and some AI assistants, never as a ranking lever.
7. Offer clean text versions of key pages where practical (Markdown or simplified HTML) and avoid burying facts in images, PDFs or carousels.
8. Write answer-first. Put a 40–60 word direct answer under each question-style heading, then expand. This also helps featured snippets and AI Overviews.
9. Make facts verifiable. Name sources, dates, authors and credentials. Statistics with citations are quoted far more often than vague claims.
10. Be consistent off-site. Same business name, description, address and profiles on Google Business Profile, LinkedIn, Crunchbase, directories and review sites. Models build entity understanding from agreement across sources.

A safe robots.txt starting point

If you want to be discoverable in AI search while keeping control over training use, a common approach is to allow retrieval bots and decide separately on training bots:

Bot names and behaviours change, so verify against each vendor’s current documentation before editing a production robots.txt.

llms.txt: what to put in it

• An H1 with your brand name and a one-sentence description.
• A short summary paragraph: who you serve, where, and what you do.
• Sections of links with one-line descriptions: services, pricing, guides, case studies, contact.
• An optional llms-full.txt with full text of key pages for models that want it.

You can see a live example at awidigital.com/llms.txt.

Common mistakes

• Blocking AI crawlers by accident through a CDN “bot fight” setting.
• Relying on a client-side-rendered React app with no pre-rendering.
• Schema that contradicts the page (wrong prices, fake reviews). This can trigger manual actions.
• Thin, generic AI-written content with no original data. Models and search engines both discount it.
• Ignoring Bing.

How to measure results

Track referral traffic from chatgpt.com, perplexity.ai, gemini.google.com and copilot.microsoft.com in Google Analytics; check server logs for AI bot hits; and run a fixed set of 20–50 buyer-intent prompts monthly across ChatGPT, Perplexity, Gemini and Claude, recording whether your brand is mentioned, cited and recommended. This is the same tracking we include in our AI SEO service.

Frequently asked questions

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