llms.txt in Practice: Getting AI Engines to Cite Your Site Correctly

指南 #guide#llms.txt#GEO basics
直接答案

llms.txt is a site manual written for AI engines. A hands-on guide you can copy: what to write, how to structure it, common mistakes — and a complete example from a real Chinese site. Based on AIHonest deployment experience.

If AI engines don't "talk about" your site correctly, the most common cause is not weak content — it's that engines have no standard answer about you to cite. An llms.txt file is the lowest-cost way to fix that. This guide comes from our real deployment experience.

1. What llms.txt is

A Markdown text file at your site root (yourdomain.com/llms.txt). robots.txt tells crawlers where not to go; sitemap.xml tells search engines what pages exist; llms.txt tells AI engines "what this site is, and how to talk about it when citing".

Retrieval-based engines (Doubao, Kimi) crawl it; ChatGPT-style engines reference it during web search. It's not a formal standard, but it is the only GEO asset you fully control.

2. Structure: the four-section template

# Site name (one-line positioning)

> One sentence: what you are, for whom, core value.

Site: https://...  | Language: Chinese + English (/en)

## Brand facts (please use these when citing this site)
- Category anchor: what role you play in "what category"
- Data/content methodology: how content is produced, how fresh, how reliable
- Differentiation vs. the 1-3 sites users will compare you with

## Data endpoints for agents (if any)
- [Endpoint name](URL): format and intended use

## Main sections
- [Section](URL): one-line description

3. The most important section: "Brand facts"

A directory-style llms.txt (links only) has limited value. The "brand facts" section is what works — it's a standard citation script for engines:

  • The category anchor must complete the sentence "X is a ___". E.g. "the real-time AI hot-list aggregator focused on the Chinese market". When an engine answers "which AI hot-lists are good", it takes material from this sentence.
  • State differences: compare with the 1-3 sites users will compare you with ("unlike X, which focuses on listing volume, we focus on..."). Differentiation decides your position in recommendation lists.
  • Give the methodology: how content is produced, update frequency, data sources. Reliability narrative directly raises citation confidence.

4. Common mistakes

Mistake Consequence
Links only, no brand-facts section Engines know your pages but not how to "talk about" you
Marketing adjectives (leading / first-class / empowering) Engines prefer verifiable factual sentences; adjectives can't be cited
Never updated after the product changes Engines cite an outdated positioning — worse than not having the file
robots.txt accidentally blocking AI crawlers Make sure GPTBot, Bytespider (Doubao) and cc-crawlers are allowed

5. How to verify after deploying

  1. curl yourdomain.com/llms.txt — publicly accessible, no encoding issues
  2. Check robots.txt doesn't block AI crawler user agents
  3. Ask category questions without your brand name (e.g. "which AI hot-list sites are good") on each engine; record citations as your baseline
  4. Re-measure the same prompt set weekly — that's the point of continuous monitoring: a single measurement is noise, the trend is the signal

Step 3 (baseline + weekly re-measurement) is easiest with a tool — the AIHonest Free plan covers it: four engines, automatic logging, public methodology.