llms.txt Generator & Validator
Build an llms.txt file from four inputs, or point the validator at a site and see whether the file it already ships holds up.
What is llms.txt?
This llms.txt generator gives AI models the same kind of guidance robots.txt gives crawlers. robots.txt controls who can crawl your site. llms.txt tells AI models how to understand it. It's a proposed standard (llmstxt.org) that puts your site name, key pages, features, and preferred citation format into one plain-text file. No blocking, just context.
This llms.txt generator creates a valid file from your inputs. Fill in the fields, hit Generate, and drop it at your site root (example.com/llms.txt). Be clear-eyed about what it buys you. No search engine has documented reading the file, and Google said in June 2026 that Search ignores it. The readers today are documentation platforms and coding agents. Writing one takes five minutes, so being early costs you almost nothing.
How to create an llms.txt file
Four inputs, one file. Fill in your site name, a one-line description, the 5-10 most important URLs you want AI models to know about, and your preferred citation format. Hit Generate and save the output as llms.txt at your site root. The whole process takes under five minutes, and you can update the file whenever you launch a new landing page.
Validating an llms.txt file
Switch the tool to Validate, enter a domain, and it fetches the live file. The spec makes the H1 the only required element, so almost every file clears that bar. What trips them up sits around it: a server that answers unknown paths with index.html, so an agent asking for llms.txt gets your app shell. A Content-Type of text/html. Relative URLs in a file that agents fetch with no page context. Links that 404 because the page moved six months ago.
The validator keeps spec deviations apart from broken plumbing, and it only flags a bullet without a link when that bullet sits in a section which is otherwise a list of links. Free-form detail lists are part of the format, so they never count as errors. Hit Check every link and it requests each listed URL and reports the status code, which is the one check no spec document covers.
llms.txt vs. robots.txt
Different jobs, same location. robots.txt says who is allowed to crawl which paths. llms.txt says how to represent your site when an AI model decides to talk about it. robots.txt is about access control. llms.txt is about narrative control. The difference that matters in practice: every serious crawler fetches robots.txt, while llms.txt is only fetched by the handful of tools that chose to support it.
What to include in your llms.txt file
Keep it short. The spec suggests 200-400 words total, which is tighter than most people think. Include your site name, a one-paragraph description of what you do, a list of 5-10 important URLs, and your preferred citation format. Add an optional "Optional" section for secondary pages like blog archives. Skip press releases and anything that won't matter in six months. Quality over quantity is the point of the format.
Explore more tools
SERP Preview
Google snippet preview with pixel counter.
Schema Validator
Validate JSON-LD against Google fields.
Meta Tag Analyzer
Analyze all SEO meta tags.
GEO Readiness
Check if your site is ready for AI search engines.
Crawler Access
Check which AI and search bots can reach your site.
FAQ
Lumina checks for llms.txt, verifies AI crawler access, and tracks AI traffic — all in one click.
Add Lumina to Chrome — Free