u/russwittman

Most "how to get cited by AI" advice stops at content and backlinks. Those matter, but there's a technical layer underneath that decides whether an assistant can even cleanly read and lift your page — and almost nobody sets it up.

The pieces that have moved the needle most in our testing:

• llms.txt — a structured summary an LLM can read at inference (the llmstxt.org standard). A cheat sheet for your site.
• ai.txt — your identity + rules for how AI can use your content. robots.txt controls access; ai.txt controls identity and use.
• ai-sitemap.xml — a sitemap with a plain-English summary and a content type per URL, so crawlers get the gist without parsing every page.
• Schema (JSON-LD) — FAQPage, HowTo, Organization, SoftwareApplication. This is what makes a passage liftable into an answer.
• A training-data / content-signal policy — e.g. search=yes, ai-input=yes, ai-train=no to allow answers but not training.

Why it's high-leverage: it ships in an afternoon and it's live on the next crawl, versus the months content and PR take. It won't replace authority signals — you still need third-party corroboration — but it removes the technical reason you're getting skipped.

Disclosure: I'm on the team that built a free, MIT-licensed generator for this whole file set, so I'm biased on how handy it is — github.com/silverbackmarketing/ai-readiness. But the point stands even if you write them by hand: these are the files worth adding.

Happy to go deeper on any of them in the comments — which of these have you actually seen change citations?

Most SEO advice for AI search stops at "make an llms.txt." After digging into how ChatGPT, Claude, Gemini, and Perplexity actually pick what to cite, I ended up deploying a fuller set of root files. Sharing the breakdown because the llms.txt-only advice is leaving a lot on the table.

The 7 categories I landed on:

• Identity & permissions — robots.txt (with per-bot rules for GPTBot/ClaudeBot/PerplexityBot) and ai.txt (who you are, what you're known for)
• Content — llms.txt (the cheat sheet) and llms-full.txt (the deep dive)
• Map & navigation — ai-sitemap.xml with plain-English summaries per page, plus a Markdown sitemap
• Intelligence — ai-entities.json, ai-intent.json (maps real user questions → your best page), ai-schema.json
• Research/RAG — rag-index.json + .jsonl so retrieval pipelines stay grounded in your content
• Policy — training-data-policy.txt, ai-disclosure.txt
• Operations — deployment checklist, manifest, structured-data guide

Two things that surprised me:

1. Serving a clean Markdown mirror of your homepage (/index.md) and pointing AI crawlers to it in robots.txt noticeably changes what gets ingested vs. the JS-heavy HTML.
2. The Princeton/Georgia Tech GEO study (KDD 2024) found citing sources and adding statistics each lift AI visibility ~37–40%. Structure alone isn't enough — the content has to be citation-shaped.

I open-sourced the generator (MIT) so you don't have to write these by hand — happy to share the link in a comment if that's allowed, or you can search "AI Readiness Kit." Curious what others here are doing beyond llms.txt — anyone tracking whether these files actually move citations?