Most teams still optimise for Google alone.
But large language models (LLMs) crawl and compress your site into internal knowledge graphs that later power AI answers. That’s a different job than just ranking URLs.
Here’s a developer-focused checklist for making your site friendlier to LLM crawlers without sacrificing SEO.
LLMs do better when core facts are:
• Short: "Starter is $99/month for 1,000 credits."
• Stable: product/tier names don’t change every quarter.
• Unambiguous: each product has one clear description.
Avoid hiding pricing, integrations or feature lists inside long narrative paragraphs.
Your FAQPage is effectively a supervised dataset of Q→A pairs.
Practical tips:
• Use real customer phrasing in the Question field.
• Keep Answer concise, factual and time-bounded where relevant.
• Avoid marketing fluff; aim for sentences that can be quoted verbatim.
Beyond title/description:
• Product / SoftwareApplication: name, description, pricing, featureList.
• Organization: legal name, logo, sameAs social URLs.
• WebSite: canonical URL, SearchAction for on-site search.
Validate via structured data testing tools and keep markup in sync with actual UI and copy.
LLM crawlers increasingly respect machine-readable contracts:
• robots.txt – allow/deny relevant user agents clearly.
• sitemap.xml – keep it small and canonical.
• llms.txt / links.txt – specify acceptable AI uses and preferred canonical URLs.
Reduce ambiguity by:
• Centralising product and plan names in config.
• Reusing the same strings across marketing site, docs and in-app help.
• Cleaning up stale routes and redirecting deprecated pages.
Pages with concrete claims are easier for AIs to cite:
• Simple stats or ranges.
• Example queries and expected outputs.
• Clear preconditions and limitations.
If you maintain a dev-focused product, a single well-written “How it works” page with structured steps and examples can dramatically improve how AIs explain your tool. As developers we’re already shaping how indexable our apps are.
Now we also shape how understandable they are to the models answering our users’ questions.