← HomeHow to Get Cited in AI
Answer

What are markdown twins and do LLMs use them?

By Jason Burns, Founder of HurcuLeads · Stuff Doer at Adolicious · Updated

Markdown twins are plain-text or markdown copies of an HTML page served at a parallel URL (typically {url}.md) so retrieval systems can ingest the content without an HTML parser — a practical convention endorsed by the llms.txt proposal but not yet formally consumed by any major answer engine.

The idea is simple: an HTML page carries navigation, ads, and layout that a retrieval crawler has to strip. A markdown twin is the same content minus the chrome, with the URL and provenance footer at the bottom. If a retrieval crawler prefers a clean copy, the twin is easier to parse; if it prefers HTML, the twin costs nothing.

The convention is inspired by llms.txt, which suggests llms.txt and llms-full.txt at the root plus optional per-page markdown files. No answer engine has publicly confirmed that it fetches twins, but the pattern has zero downside beyond disk space — the twin can be generated from the same source that renders the HTML page.

Two implementation notes. First, ship a provenance footer on every twin: canonical URL, author, published/modified dates, license. This gives any retrieval system a machine-readable attribution block. Second, do not put the twin in your sitemap — the HTML page is the canonical, and a duplicate entry in the sitemap muddles the URL structure the search engine sees.

The twin is insurance. It is not a substitute for server-rendered HTML, valid schema, or an IndexNow ping.

Related terms

Sources

Related questions people ask next

  • What is llms.txt and does it actually work?

    llms.txt is a proposed plain-text file at a site's root that lists the URLs and short descriptions a site owner wants LLMs to prioritize when summarizing that site — and as of mid-2026 no major answer engine has confirmed that it uses the file for retrieval or citation.

  • Why do canonical URLs matter for AI answers?

    Canonical URLs matter for AI answers because they tell the retrieval layer which single URL should carry the citation credit when the same content is reachable at multiple addresses — without a canonical, the model may cite the wrong copy or split authority across duplicates.

  • How should I license content for citation by AI?

    License content for AI citation by publishing an explicit license that permits quoted citation with attribution and prohibits wholesale republication — the goal is to make it legally safe for models to quote you while keeping copyright leverage against sites that copy the full text.

  • How should large sites structure their sitemap?

    Large sites should structure their sitemap as a sitemap index pointing at multiple child sitemaps, each holding up to 50,000 URLs or 50MB uncompressed, and reference the index from robots.txt so every crawler — search and AI — can discover the full URL surface in one round trip.