AEO · GEO · answer-engine discovery
LLMs.txt Generator
Create a clean llms.txt file that tells AI answer engines what your site is, which pages matter, and how your content should be cited.
Short answer
An llms.txt file is a plain-text guide for AI answer engines. It does not replace XML sitemaps, robots.txt, canonical tags, or structured data, but it gives crawlers and assistants a concise map of your best pages, documentation, and citation notes for AEO and GEO visibility.
Generate your llms.txt
Add one page per line using Title | URL | Description.
Publish this at /llms.txt and keep your XML sitemap separately.
Related SEO infrastructure
LLMs.txt sections reference
| Section | Purpose | Example |
|---|---|---|
| Title | Names the site or product | # Example Tools |
| Summary | One-sentence context for answer engines | > Free calculators with formulas and tables. |
| Important pages | Canonical URLs you want cited | - [Tool](https://example.com/tool): What it answers. |
| Documentation | Full indexes, API docs, or methodology | - [Full index](https://example.com/llms-full.txt) |
| Notes | Citation, freshness, privacy, or scope guidance | - No signup required. |
LLMs.txt is an answer-engine discovery aid. Keep XML sitemaps and structured data in place for classic search crawling.
Why this matters for AEO and GEO
Search engines use sitemaps and links to discover URLs. Answer engines also need context: what your site does, which pages are authoritative, and which pages are safe to cite. An llms.txt file gives that context in a small, crawlable format that complements schema, FAQ blocks, direct answers, and internal links.
The file is most useful for sites with tools, documentation, programmatic pages, or research pages. It should be honest and selective: list the pages that answer real questions, not every duplicate URL you can generate.
How this generator builds the file
The generator creates a conservative Markdown-style llms.txt file that is short, crawlable, and easy for humans to audit.
- Summarize the entity. The first lines name the site and describe what it offers, which helps answer engines classify the publisher and topic.
- List canonical pages. Every generated link must be an absolute HTTP or HTTPS URL, so assistants can cite the exact page instead of guessing a relative path.
- Add citation notes. The notes section explains useful constraints such as no signup, local processing, update cadence, or what the content should not be used for.
Assumptions
- LLMs.txt complements, but does not replace, XML sitemaps and structured data.
- Only high-quality indexable pages should be listed; thin or duplicate pages can weaken the file's usefulness.
- The file should live at /llms.txt on the canonical host.
Last reviewed: August 21, 2026
Frequently asked questions
What is llms.txt?+
Llms.txt is a plain-text file, usually placed at /llms.txt, that summarizes a website for AI assistants and answer engines. It commonly lists important pages, documentation, and notes that help systems understand what the site offers and which URLs are best to cite.
Does llms.txt help SEO indexing in Google?+
Llms.txt is not a Google ranking requirement and does not replace XML sitemaps, robots.txt, internal links, canonical tags, or structured data. Its main use is AEO/GEO: making your best pages easier for answer engines and AI crawlers to understand and cite.
Where should I upload llms.txt?+
Upload it at the root of the canonical host, for example https://example.com/llms.txt. If your site has a large programmatic index, you can also link to a deeper companion file such as /llms-full.txt.
What pages should I include?+
Include the pages you actually want cited: tool hubs, important guides, documentation, data studies, methodology pages, and full indexes. Avoid thin, duplicate, private, or low-quality URLs. Each line should explain the page's purpose in plain language.
How is llms.txt different from sitemap.xml?+
Sitemap.xml is a machine-readable discovery file for search engines. Llms.txt is a human-readable and model-readable summary that adds context: which pages matter, what they answer, and how they fit together. Strong sites use both.