Free llms.txt Generator

llms.txt is a plain-text file placed at the root of your website, at yourdomain.com/llms.txt, that tells large language models what your business is, what you offer, and which pages matter. It was proposed in 2024 by Jeremy Howard of Answer.AI as a way to give AI systems a clean, curated summary of a site instead of forcing them to reconstruct meaning from navigation menus, cookie banners, scripts, and marketing chrome.

The format is deliberately simple: markdown, with an H1 carrying your site or company name, an optional blockquote summary, and then sections of annotated links to your most important pages. Each link gets a short description explaining what the reader will find there. It is human-readable, machine-parseable, and takes minutes to deploy.

Think of it as the elevator pitch you hand to every AI system that encounters your business, written by you rather than inferred from your homepage.

Because a growing share of B2B research now begins with an AI assistant rather than a search box. Buyers ask ChatGPT, Claude, Perplexity, Gemini, or Copilot to compare vendors, explain categories, and shortlist options, and they act on the answer. If the model's understanding of your company is vague or wrong, you are not in the shortlist, and unlike a bad search ranking you never see the impression you lost.

The risk is specifically acute in B2B. Your positioning is nuanced, your category naming is often contested, and your differentiators are the kind of thing a model will flatten into a generic sentence if left to infer them from a JavaScript-heavy homepage. An llms.txt lets you state, in your own words, what you do, who you serve, and how you are different.

It is also one of the cheapest marketing assets available. One file, deployed once, that improves how every AI system describes your business.

It goes at the root of your domain, served at https://yourdomain.com/llms.txt, exactly like robots.txt. It must be reachable without a login, return a 200 status, and be served as plain text or markdown. Subdomains need their own file: a file on your main site does not cover docs.yourdomain.com.

How you deploy depends on the stack. On WordPress, upload it to the site root via FTP or your host's file manager, since some plugins can also handle it. On Webflow, use the custom code and hosting settings or a redirect to a hosted file. On Next.js, drop it in the /public directory. On Hubspot, use the file manager and confirm the served URL is at the root, not under a hashed CDN path.

After deploying, open the URL in a private browser window. If you see the raw text, you are done. If you see your 404 page or a download prompt with the wrong content type, the file is not correctly served and no AI system will read it.

No. The three do different jobs and you want all three:

• robots.txt is permission. It tells crawlers, including AI crawlers such as GPTBot, ClaudeBot, PerplexityBot, and Google-Extended, what they may and may not access. It controls behavior but conveys no meaning.
• sitemap.xml is inventory. It lists every URL on your site so a crawler can find them all. It is exhaustive and unranked, which is precisely what makes it unhelpful as a summary.
• llms.txt is meaning and priority. It is curated, not exhaustive: a short, opinionated explanation of what your business is and which pages matter most, written for a reader that has to compress your site into a few sentences.

The critical companion check is robots.txt. If it blocks AI crawlers, your llms.txt is a locked door with a welcome mat. Confirm your allow rules before you worry about anything else.

Be honest about the state of play: llms.txt is a proposed convention, not a ratified standard, and no major AI provider has publicly committed to consuming it as a ranking or retrieval input. Anyone who tells you it guarantees citations is selling something.

What is true is that it costs almost nothing and carries real asymmetric upside. Adoption is growing quickly across developer-facing and B2B sites, agentic browsing tools increasingly fetch predictable root-level files, and the file is plain markdown, which means any model that does retrieve it gets an unusually clean, high-signal description of your business. If the convention is adopted broadly, you are already positioned. If it is not, you have lost an hour.

Treat it as one layer of an AI visibility program, not the whole program. The layers that reliably matter today are clean crawlable HTML, genuine subject-matter depth, structured data, and being cited by sources models already trust.

Structure it as markdown, in this order:

• An H1 with your company or site name.
• A blockquote summary, one or two sentences: what you do, who you serve, and what makes you different. This is the single highest-value line in the file, because it is the sentence most likely to be repeated back about you.
• Short context, a paragraph or two on your category, your customers, and any terminology a reader needs.
• Sections of annotated links grouped by intent: Product, Documentation, Pricing, Customers, About. Each link carries a short description of what it contains.
• An optional section for material that is genuinely lower priority.

Write plainly and skip the marketing adjectives. "The leading innovative platform" tells a model nothing it can use. "Content repurposing software for B2B marketing teams that turns one source asset into every LinkedIn format" is specific enough to be retrieved against a real question. Curate ruthlessly: 15 to 30 well-chosen links beat a mirror of your sitemap.

llms.txt is an index: a concise, curated map with links out to the detail. llms-full.txt is the whole corpus, the actual content of your key pages inlined into one large markdown file, so a model can consume everything in a single fetch without following links.

Start with llms.txt. It is small, easy to maintain, and appropriate for almost every marketing site. Add llms-full.txt only if you have genuinely deep technical documentation and want an agent to be able to answer detailed implementation questions without crawling, which is why it is most common on developer tools and API products.

The trade-off is maintenance. A full file goes stale fast, and a confidently wrong answer sourced from your own outdated file is worse than no file. If you cannot commit to regenerating it when your product changes, ship the index only.

Review it quarterly, and update it immediately after any of these: a positioning or messaging change, a new product or major feature, a pricing change, a rebrand, a site restructure that changes URLs, or a new category of content worth surfacing.

Stale files cause real damage, because an AI system quoting your own file about a product you discontinued is more credible, and therefore more harmful, than a vague guess. Broken links in the file are worse still, since a retrieval that 404s teaches the system to trust the file less.

Put the review on the same schedule as your homepage messaging review, and add "update llms.txt" to your launch checklist alongside the press release and the pricing page. It takes minutes and it keeps the asset honest.

Generative engine optimization is mostly the disciplined version of things good content teams already know:

• Answer real questions directly. Lead with the answer in the first two sentences, then explain. Models extract answers, not build-ups.
• Be specific and quotable. Numbers, named methods, dated benchmarks, and concrete examples get cited. Adjectives do not.
• Publish structured data. Organization, Product, Article, and FAQPage schema all help machines parse what a page asserts.
• Get mentioned where models already look. Review sites, industry publications, community discussions, and comparison pages carry disproportionate weight.
• Keep content server-rendered. If your key copy only appears after JavaScript executes, many crawlers never see it.
• Own your comparison pages. If you do not publish an honest comparison against your alternatives, someone else's version is what gets quoted.
• Keep facts consistent across your site, your profiles, and third-party listings. Contradictions lower confidence, and low confidence means no citation.

It does neither directly. Google has not indicated that llms.txt is a ranking signal, and the file is not a substitute for anything in a conventional SEO program. Keep your sitemap, your robots.txt, your structured data, your internal linking, and your page speed exactly as they are.

The indirect benefit is real, though. Writing an llms.txt forces a clarity exercise most marketing teams skip: stating in two sentences what the business does, for whom, and why it is different, and then deciding which 20 pages actually matter. Teams routinely discover during that exercise that their homepage never says plainly what they sell, or that three important pages are orphaned. Fixing those helps human search too.

There is also no downside risk. The file exposes nothing that is not already public, it does not affect crawl budget meaningfully, and search engines that do not recognize it simply ignore it.

Start by asking. Build a list of 20 to 30 questions a real buyer would type, including category questions ("best content repurposing tools for B2B"), comparison questions, and direct questions about your company, then run them across ChatGPT, Claude, Perplexity, Gemini, and Copilot. Record whether you appear, how you are described, and which sources are cited. Repeat monthly and watch the trend, not any single answer.

Then instrument what you can. Referral traffic from chat.openai.com, perplexity.ai, and similar hosts shows up in analytics. AI crawler hits from GPTBot, ClaudeBot, and PerplexityBot appear in server logs and tell you what is being fetched and how often. Self-reported attribution on your demo form, the plain "how did you hear about us" field, is unfashionable and remarkably effective here, because AI-sourced buyers often say so explicitly.

Pay attention to how you are described, not just whether you appear. A confident, wrong description is the problem llms.txt exists to fix.

It is free, and it needs two things: your website URL, so we can read your publicly available pages exactly as a search engine would, and your email address, so we can send the finished file. We only read pages that are already public. Nothing behind a login is touched.

The generated file is available on a download link that stays live for 24 hours, after which the file is permanently deleted. Your email is handled under our Privacy Policy, and we will occasionally tell you what DesignTech AI can do. The file itself is yours to edit: treat what we generate as a strong first draft, then sharpen the summary line in your own words, because nobody can describe your positioning better than you can.