LLMs.txt: All You Need To Know About It

llms.txt is a concise, curated Markdown file at yourdomain.com/llms.txt that guides AI to your canonical pages, improving answers and citations. Create, organize, and update it; curate links, avoid noise, and treat ranking gains as indirect.

Key takeaways:

• llms.txt = human-curated map for AI answers.
• Lives at /llms.txt (site root).
• Markdown; sections + brief link blurbs.
• Purpose: steer AIs to canonical pages → better citations, fewer hallucinations.
• Not a ranking cheat; indirect gains only.
• Workflow: inventory → write → publish → QA → maintain.
• Best practices: be selective; one canonical per topic; clear blurbs; align with robots; keep fresh.
• Avoid: treating like robots.txt, dumping sitemaps, stale/duplicate links.
• Optional: llms-full.txt for deep content.
• Adoption: growing, not universal.

“llms.txt” is one of those new-ish things everyone keeps hearing about but few have fully wrapped their heads around. Here’s the complete picture, no fluff.

What is llms.txt (in plain English)

llms.txt is a plain-text Markdown file you publish at https://yourdomain.com/llms.txt. It’s a curated, LLM-friendly map of your most important content: a short intro, then organized links (ideally to clean, text/Markdown pages) with one-line descriptions. It was proposed by Jeremy Howard (Answer.AI) in Sept 2024 to help AI assistants use your site at answer time (not to crawl it like search bots).

Think of it as a hand-crafted sitemap for AI, not a rulebook like robots.txt. Its job is to point models to high-quality, canonical sources you want them to read/cite when answering questions about your brand, docs, products, or policies.

There’s also an optional companion, llms-full.txt, which inlines much more of your content in one big Markdown file (useful for tooling and code assistants, but watch size). You’ll see live examples in the wild (e.g., Perplexity’s docs).

Does it help you “rank” in LLMs?

Short answer: it can help LLMs find, understand, and cite your best pages — but it’s not a guaranteed ranking lever (there’s no universal “LLM SERP” yet). Some platforms and ecosystems are experimenting (e.g., Anthropic/Perplexity interest; CMS/SEO tooling support), while others haven’t publicly committed, so treat it as smart enablement, not magic SEO.

Practically, sites adopting llms.txt report benefits like:

• fewer hallucinations (models hit your canonical pages first),
• cleaner citations to your brand docs,
• better “answer coverage” for FAQs/products/policies.
  These are consistent with how the spec is designed to work (give models clean, structured entry points).

What good llms.txt files include

• Title + 1–3-sentence summary of what your site/product is.
• Sections for your key topics (Docs, Pricing, Legal, Support, Blog Highlights, Press, etc.).
• Bulleted links to the canonical page for each topic, each link with a short, descriptive blurb.
• Prefer text/Markdown (or very clean HTML) targets to reduce noise.
• Optional: a “What not to use” note (deprecated pages), and a “Contact/updates” line.
  These are straight from the proposal and current best-practice write-ups.

A ready-to-use template (copy → edit → upload as /llms.txt)

# Your Project / Brand Name
> One-sentence what/why. Another sentence with scope or audience.

## Start here
- [Overview](https://example.com/overview.md): What the product is and who it’s for.
- [Quickstart](https://example.com/docs/quickstart.md): 5-minute setup with links to next steps.

## Product & docs
- [Core Concepts](https://example.com/docs/concepts.md): Key terms and mental model.
- [How-to Guides](https://example.com/docs/how-to.md): Tasks with step-by-step instructions.
- [API Reference](https://example.com/docs/api.md): Endpoints, auth, rate limits.

## Pricing & policies
- [Pricing](https://example.com/pricing.md): Plans, limits, billing cycles.
- [Terms of Service](https://example.com/legal/tos.md): Legal terms.
- [Privacy](https://example.com/legal/privacy.md): Data collection & processing.

## Support & status
- [FAQ](https://example.com/support/faq.md): Top issues and fixes.
- [Contact](https://example.com/support/contact.md): Email/chat/escalations.
- [Status](https://status.example.com): Reliability & incident history.

## Company & press
- [About](https://example.com/about.md): Mission, timeline.
- [Press kit](https://example.com/press.md): Logos, boilerplate, media contacts.

---
Last updated: 2025-08-11 • Maintainer: content@example.com

Tip: if you have deep technical docs, consider also publishing /llms-full.txt (a larger, single Markdown file that consolidates the most important docs for tools/agents), but keep an eye on size. LangChain

Step-by-step: how to create and ship it (30–60 minutes)

Inventory your canon

List the 20–60 pages you want models to hit first (docs, FAQs, pricing, legal, product pages). Trim duplicates and “OK-to-skip” pages.

Prefer clean targets

Where possible, link to Markdown or clean HTML (strip heavy nav/JS). If you’re on a docs platform like Mintlify, many already generate both.

Write the file in Markdown

Use the template above. Keep link blurbs short (≈8–14 words). Group by intent (learn, build, buy, get help).

Publish at the root

Save as llms.txt and host it at https://yourdomain.com/llms.txt. Many CMS/hosts now support dropping this into the root easily (e.g., Webflow added first-class support).

(Optional) add llms-full.txt

Generate a single, larger Markdown doc with the most useful long-form content. Link to it from llms.txt under “For agents/tools.”

QA like an AI

• Open the file in a browser: is it fast, readable, scannable?
• Ask an AI assistant: “Use the content in https://yourdomain.com/llms.txt to answer…” and see what it cites/uses.
• Keep it fresh (update when docs/pricing/policies change). The spec itself even recommends testing with multiple models.

Best practices (what actually moves the needle)

• Be selective, not exhaustive. Prioritize signal over listing every URL (models have context limits).
• One canonical target per task/topic. Reduces contradictory answers and broken citations.
• Use descriptive, human summaries for each link. This massively helps retrieval/ranking inside the model’s context.
• Keep robots.txt and llms.txt logically aligned. If you block an area for AI user-agents in robots, don’t point to it in llms.txt (polite bots won’t fetch it).
• Add a “What not to use” or “Deprecated” section. Nudge models away from outdated pages.

Mistakes to avoid

• Treating it like robots.txt (it’s not an allow/deny file). It’s guidance/curation, not enforcement.
• Dumping your whole sitemap. Long, noisy files underperform. Curate.
• Forgetting legal/policy pages. These are commonly asked about and often misrepresented by AIs.
• Letting it go stale. Out-of-date pricing/limits cause wrong answers and lost trust.

Where adoption stands

• The idea is widely discussed; tooling/CMS support and some early adopters exist.
• It’s not a formal, universally enforced standard yet; some analyses show mixed/limited parsing across major AI platforms, so results vary. Plan for upside, but don’t bank KPIs solely on it.

Quick FAQs

Q: Should we link from robots.txt to llms.txt?

Not required; just ensure llms.txt lives at the root and is publicly reachable. (Some folks add an FYI comment or a normal <link rel="..."> on docs pages, but it’s optional.)

Q: Do we need Markdown targets?

Preferred, yes (cleaner input → better answers). If you only have HTML, keep it lightweight and clearly structured.

Q: How big can it be?

Keep llms.txt short and pointed; if you need depth, put it in llms-full.txt and link it.