Why llm.txt exists
llm.txt is a community proposal for a lightweight, machine readable summary of a website. The idea is simple: give language models a short, structured overview of the most important pages and what each page contains. It is not a search engine standard, and it is not an official requirement from any major platform. It is a proposal meant to make sites easier to interpret.
The best public source for the spec is the llms.txt repository. It describes a Markdown based format, a recommended location at the site root, and a focus on clarity and brevity. It also frames llm.txt as a supplement, not a replacement, for existing content and metadata.
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llm.txt is optional, not a ranking requirement
It is tempting to treat llm.txt as a ranking signal, but no major search platform says that. Google is explicit that AI Overviews and AI Mode do not require special markup or AI text files. That statement appears in the AI features documentation. If you create llm.txt, it should be because it helps clarity, not because you think it unlocks visibility.
This matters for planning. You still need to be indexable, and you still need clear, useful pages. llm.txt does not replace that. It is more like a summary sheet that helps a model understand what is already on your site.
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What the llm.txt proposal actually specifies
The proposal describes a Markdown file that typically sits at /llm.txt. It uses a simple structure: a title, an optional description, and a list of key pages with short explanations. It also supports sections such as "Optional" or "Ignored" to show which pages are not core to the summary.
The repo also describes a companion concept called llms-full.txt, which is intended to give a full content dump for systems that want more detail. The shorter llm.txt is meant to be a guide, not a mirror of your whole site.
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When llm.txt helps
llm.txt is most useful when your site is complex and the core offer can get buried. Service sites often have the same issue: the key pages are obvious to humans, but models do not always find them. A concise summary can help a model focus on your actual offer instead of a random blog post.
This is especially helpful if you serve multiple regions or multiple service types. A short summary that names your core offer, your target audience, and your proof pages can reduce ambiguity. It does not guarantee citations, but it can make your site easier to interpret.
The key is to keep it honest. llm.txt should describe what is on the site, not a new version of your offer. If you write a summary that does not match the page content, you are creating contradictions that make citations less likely.
When llm.txt does not help
llm.txt is not a replacement for a strong services page. It will not fix vague messaging, thin proof, or confusing navigation. If your primary pages are unclear, a summary file just points a model to unclear pages.
It also will not help if your site is blocked by robots rules or hidden behind a login. Models can only cite what they can access. That is why your access rules and your core pages still matter more than any summary file.
How to structure llm.txt for a service business
Keep it short. A good llm.txt file is closer to a table of contents than a sales deck. It should answer three questions: what you do, who you do it for, and where the proof lives.
A simple structure might include:
- a one sentence summary of the company
- a short description of the primary service
- links to the services page, the main offer page, and proof pages
- a short note about the regions you serve
You do not need to include every blog post. In fact, including too many links defeats the purpose. The goal is to guide a model to the right pages, not to show it everything.
If you need help deciding which pages are core, start with your services page and your main offer page such as business websites. Then add proof pages like case studies and reviews. A focused FAQ can also help, because it answers the questions buyers ask AI systems.
A simple template you can adapt
You do not need a complicated format to make llm.txt useful. A small, readable structure is enough. Here is a simple template that follows the intent of the proposal without copying any specific example. Customize the wording to match your actual pages.
# Company name
We design and build marketing websites for B2B service firms.
## Core pages
- /services: Overview of services and fit
- /services/business-websites: Core offer and process
- /case-studies: Proof of outcomes and scope
- /reviews: Client feedback and trust signals
- /faq: Common buyer questions and answers
## Optional
- /blog: Articles and guidance
This is intentionally short. It gives a model enough context to find your core pages without drowning it in secondary links. If you are tempted to add more, ask yourself whether the page helps a buyer make a decision. If it does not, it probably does not belong in the summary.
How to choose which pages to include
The safest rule is to include pages that explain your offer and pages that prove it. Everything else is optional. If you only include one page, make it your core service page. If you include five, make sure each one answers a different buyer question.
Avoid adding pages just because you want them to rank. llm.txt is not a ranking file. It is a summary file. A focused summary is more useful than a long list of links.
If your site has multiple services, include only the primary offer you want to be known for. If you include every service, the summary becomes a catalog and the model has to guess which one matters. It is better to be clear about your primary offer than to be vague about all of them.
How to handle multiple services or regions
If you run a multi service business, treat llm.txt as an editorial choice. Pick the service you most want to be associated with and make that the center of the summary. You can still include secondary services, but keep them in a short optional section so the core message stays dominant.
If you serve multiple regions, add one line that makes that explicit. A single sentence like "We serve US, UK, EU, and APAC teams, with English first delivery" is often enough. That helps a model resolve location queries without forcing you to build separate regional summaries.
Do not try to build separate llm.txt files per region unless you have separate domains or subdomains. In most cases a single, clear summary is more useful than a fragmented set of summaries that drift over time.
Multilingual sites need one clear primary summary
If your site exists in multiple languages, the safest approach is to publish one llm.txt that matches your primary language and primary business offer. That does not mean you ignore other languages. It means you avoid creating conflicting summaries that say slightly different things.
If you have truly separate language domains, you can publish a language specific llm.txt on each domain. But if your site uses language subpaths, keep one summary that points to the canonical language you want to be known for. You can still link to language versions inside the file, but do not duplicate the entire summary for each language. That creates ambiguity instead of clarity.
The goal is to give models one clear source of truth. If you present multiple versions of the same summary, you are asking the model to choose between them. That is rarely helpful.
Keep it consistent with your core messaging
The worst outcome is a summary file that says one thing while your website says another. That creates confusion for AI systems and for people. The fix is simple: write your llm.txt summary by paraphrasing the first screen of your main services page. If the summary and the page match, the file reinforces clarity. If they do not match, the file amplifies confusion.
This is also why you should avoid stuffing llm.txt with marketing language. Use the same language you use on your site. If you call your offer "marketing websites," use that term everywhere. Consistency makes citations more likely because the system sees the same statement repeated across pages.
Treat llm.txt as a content maintenance task
llm.txt will get stale if you do not own it. A good pattern is to make one person responsible for it and update it whenever you update your core pages. If you change your service scope, update the summary. If you add a new case study, decide whether it should be listed.
This does not need to be a heavy process. A short checklist is enough. The goal is to keep the file aligned with the pages it references. A stale summary is worse than no summary because it points models to the wrong story.
If you already run a content calendar, add llm.txt to it. Treat it like a high level meta page. It does not need weekly updates, but it should move when your offer moves.
Assign ownership and approve changes
Because llm.txt summarizes your offer, changes to it should be treated like changes to your positioning. If multiple people can edit it without oversight, it will drift. The safest approach is to make one person accountable for final approval.
This can be your marketing lead, your founder, or whoever owns the brand narrative. The important part is to keep one consistent voice and one consistent set of terms. That protects you from small contradictions that add up over time.
What to exclude on purpose
Not everything belongs in llm.txt. Do not list internal pages, draft content, or pages that are not meant to represent your public offer. If you have gated resources, keep them out of the file. If you have campaign pages that use short lived messaging, keep them out too.
llm.txt is a public summary. Anything you put in it should be accurate and durable. If a page will be replaced in three months, it should not be part of the summary.
Common mistakes to avoid
The most common mistake is copying your sitemap into llm.txt. A sitemap is a discovery file. llm.txt is a summary. If you include everything, the summary stops being useful.
Another common mistake is using marketing fluff instead of facts. If the file reads like a pitch, a model has no clear signal to extract. The best summaries are concrete and boring. They say what you do, who you do it for, and where the proof is.
Finally, do not create a summary that contradicts your core pages. If the summary says you focus on B2B services but your services page lists consumer projects, the model will not know which statement to trust. Fix the page first, then write the summary.
Avoid keyword stuffing
Some teams treat llm.txt like a list of keywords. That is a mistake. A list of keywords does not help a model understand what you actually do. It only adds noise.
If you want to include industry terms, do it inside a sentence that explains your offer. A short paragraph that says "We build marketing websites for B2B service firms, including professional services, SaaS consultancies, and agencies" is far more useful than a block of disconnected keywords.
Clarity beats density. If the summary reads like a human description, it will be more useful to a model than any keyword list.
Do not confuse llm.txt with structured data
llm.txt is a human readable summary for models. Structured data is machine readable metadata for search engines. They serve different purposes. Structured data can help clarify entities and relationships in the page content. llm.txt can help models find the right pages in the first place.
You should use both. Keep your structured data accurate and current, and use llm.txt as a lightweight summary for models that want a fast overview. If you need to review your structured data, the JSON-LD generator is a quick way to check what your pages are outputting.
Where llm.txt fits alongside sitemaps and RSS
A sitemap tells crawlers what exists. RSS feeds tell subscribers what is new. llm.txt tells a model what matters. Those are different jobs. If you already have a sitemap and an RSS feed, do not replace them. llm.txt is additive.
If you are worried about duplication, remember that the file is not a list of everything. It is a list of what should matter to a buyer. That focus is what makes it different from a sitemap.
Use llm.txt to reinforce internal links, not replace them
Models do not only use llm.txt. They still read your pages, and they still follow links. That means internal linking is still essential. The summary should point to your core pages, and those pages should point to each other.
This is how you avoid orphaned pages. If llm.txt points to a case study, make sure the case study points back to your services page. If it points to a FAQ, make sure the FAQ links to the offer page. A summary that leads to dead ends is a missed opportunity.
Think of llm.txt as a shortcut into your existing internal link structure. If the structure is weak, fix the structure first.
Use llm.txt alongside robots and access rules
Because llm.txt is a root file, it lives alongside robots.txt. Make sure those two files do not contradict each other. If your robots rules block key pages, llm.txt will point to pages that a model cannot access.
If you need to review your robots rules, the robots.txt generator can help you verify what is allowed and what is blocked. The goal is not to open everything. The goal is to make sure the pages you want cited are actually accessible.
When you should skip llm.txt
If your site is intentionally private, do not publish llm.txt. A public summary is only useful when you want public visibility. If your offer is confidential or your pages are behind authentication, a summary file does not help and may create a security headache.
You should also skip it if your core messaging is in flux. A summary file makes your positioning feel more fixed. If you are still deciding who you serve or what you offer, it is better to settle the messaging first and then publish the summary.
Finally, skip it if you are not prepared to keep it updated. A stale summary is worse than none because it misleads models and buyers. If you cannot commit to maintenance, focus on your main pages instead.
If you work in a regulated industry, check with legal before publishing any new summary file. Even though llm.txt only points to public pages, some industries have strict rules about how services can be described. A summary file is still a public representation of your offer. Treat it with the same care you apply to your homepage copy.
How to discuss llm.txt with stakeholders
If you present llm.txt to a client or a team, keep expectations grounded. It is not a guarantee of citations. It is a clarity tool. It helps models find the right pages, but it cannot fix vague content or poor proof.
The most useful framing is to treat it as a short summary that doubles as internal alignment. Stakeholders can read it to confirm the offer and the proof points. Models can read it to find the right pages. That is the real value.
When expectations are set correctly, the file becomes a small but helpful part of a broader visibility plan instead of a magic solution.
How to check that llm.txt is accessible
The basic test is simple: open /llm.txt in a browser and make sure it loads without redirects, errors, or authentication. If it does not load for a regular user, it will not load for an automated system either.
You can also confirm that your file is not blocked by robots rules or access controls. If you use a CDN, make sure the file is not cached behind an authentication layer. If you use a static site build, make sure the file is included in the public output. These are boring checks, but they prevent the most common failures.
How to evaluate whether it helps
llm.txt is not a direct ranking metric, so measuring impact is subtle. The best signal is whether your core pages get cited more often in AI answers after you publish it. That is not guaranteed, but it is the outcome you can observe.
If nothing changes, do not assume the file is useless. It may simply mean your core pages are still too vague to cite. llm.txt can only point to what exists. If the destination is weak, the summary cannot fix it.
If you want to track citations across systems, keep a small prompt set and run it monthly. The goal is not statistical proof. The goal is directional feedback. If the citations move toward your core pages, the file is likely helping.
How to iterate without overthinking it
If you publish llm.txt and nothing changes, do not rewrite it five times in a week. The file is a summary, so the real lever is still your core pages. Use the feedback to improve those pages first.
Once the pages are clearer, update the summary to match. That is usually enough. You should not need to rework the file constantly. The best llm.txt files are stable because the underlying offer is stable.
If your offer changes, update the file. Otherwise, let it sit. The goal is consistency, not churn.
If you have a blog heavy site
Many sites in this space have dozens of blog posts. That is useful for discovery, but it can also dilute clarity. If your llm.txt file lists too many posts, the summary becomes a feed instead of a guide.
The solution is to include only the posts that define your position. A single cornerstone article that explains your approach can be enough. The rest should be discoverable through your site navigation, not through the summary file.
If you want to include a blog link, point to your blog index and let the model decide. The purpose of llm.txt is not to rank posts. It is to point to the pages that explain who you are and why you are credible.
A quick review checklist before publishing
Before you publish, read the file top to bottom and ask a few simple questions. Does it match your services page? Does it point to proof that supports your claims? Is it short enough to scan in under a minute? If any of those answers are no, fix the file before you ship it.
Also check for accidental contradictions. If your services page says you work with B2B clients and the summary says you work with startups in general, align the language. If your services page says you focus on marketing sites and the summary says you build full stack platforms, align the scope. Small differences can create big confusion in AI summaries.
Finally, make sure the file loads quickly and without redirects. A 404 or a redirect chain is a silent failure. A clean, fast response is the simplest way to make the file usable.
A practical workflow to publish llm.txt
The simplest workflow is to treat llm.txt as part of your content maintenance cycle. When you update your services page, update the summary. When you publish a new case study, decide whether it should be included. When your offer changes, reflect the change in the file.
Keep the file short and readable. A model should be able to scan it in seconds. If it feels like a full site map, it is too long. If it reads like a sales pitch, it is too vague.
Handling multiple brands or offers
If you operate multiple brands under one domain, you have a decision to make. You can either summarize the primary brand and leave the others out, or you can treat the file as a parent summary that links to the brand pages. Either way, you should avoid blending different offers into one description.
The cleaner option is usually to focus on one primary offer. If a buyer asks an AI system about your company, you want the model to find one clear description, not a mixed set of brands with different audiences. If you need to represent multiple brands, separate them into clearly labeled sections and keep the descriptions short.
Use llm.txt during a redesign or repositioning
When you redesign a site, your pages often change faster than your public narrative. That is when llm.txt can help, because it lets you restate the new positioning in one place while the rest of the site catches up.
This does not mean you should use llm.txt to paper over incomplete pages. It means you can update the summary at the same time as your core pages to keep the public story aligned. If the redesign is in progress and pages are not yet updated, wait. A summary that points to outdated pages is worse than no summary at all.
If you are changing your offer, treat llm.txt as the final step. Update the services page, update the proof pages, then update the summary. That order keeps the file aligned with the pages it links to.
Keep sensitive information out of the summary
llm.txt is public. Do not put private pricing, client names under NDA, or internal process details in the file. The file should only include information you are comfortable publishing on your website.
If you need to communicate sensitive details, do it on gated pages or in direct conversations. The summary should point to the public surface of your offer, not your internal playbook.
What success looks like over time
Because llm.txt is experimental, success looks subtle. It might mean your core services page is cited more often in AI answers. It might mean fewer citations that point to random blog posts. It might simply mean that when someone asks an AI system about your business, the summary it produces is closer to your actual positioning.
Those are not dramatic changes, but they are real. The goal is not to chase a spike in traffic. The goal is to make your public story more consistent across AI systems. That is a long term benefit that compounds as more people use AI search.
If you want a more concrete signal, track citations for a handful of buyer questions every month. If the citations move toward your core pages after you publish llm.txt, the file is likely helping. If they do not move, your pages probably still need clarity.
Use llm.txt to support sales alignment
llm.txt is not just for models. It can also serve as a quick alignment document for your team. If a new team member reads the file, they should understand the core offer in under a minute. If they do not, the file is too vague.
This is useful in sales. When a buyer arrives from an AI citation, the sales team needs to know exactly what was promised on the site. llm.txt can act as a snapshot of that promise. It should match the first screen of your services page and the key proof points you use in conversations.
Treat it as a source of truth. If a proposal says one thing and llm.txt says another, you have a problem. The file is a small checkpoint that keeps your external narrative and internal narrative aligned.
How to keep proof pages in sync with the summary
Your summary should point to proof pages that actually demonstrate the claims you make. If you list a case study, make sure the case study supports the promise. If you say you specialize in a specific industry, make sure at least one proof page shows that work.
If the proof does not exist, do not include the claim. This is a simple rule that prevents overreach. It also makes your citations more trustworthy, because the linked pages actually validate the summary.
When you publish a new case study, decide whether it changes your positioning. If it does, update the summary. If it does not, you can leave the summary as is. The file should reflect the long term story, not every new project.
How to use llm.txt with campaigns
Campaign pages often use a different tone than core pages. That is fine for paid campaigns, but it can create confusion for AI systems. The safest approach is to keep llm.txt focused on your core offer and let campaigns link back to that offer.
If you run a campaign that changes your positioning for a limited time, do not rewrite llm.txt for it. Campaign language should live in the campaign page, while llm.txt stays stable. That stability keeps your broader visibility consistent even when campaigns come and go.
If you want a campaign to influence AI visibility, make sure the campaign page points clearly to the canonical services page. That way the model can still find the stable source even if it sees the campaign copy.
How llm.txt fits with your conversion path
Your llm.txt summary should point to pages that already convert. If your services page is weak, fix that first. If your proof pages are thin, strengthen them before you highlight them. A summary file can amplify your strengths, but it will also amplify your weaknesses.
That is why the conversion path matters. Your project brief and book a free call paths should be obvious on the pages you list. If a model sends traffic to a page and the next step is unclear, the opportunity is lost.
What to expect after you publish
Publishing llm.txt will not change your rankings overnight. There is no official promise that it will change anything at all. Think of it as a clarity layer. It can help a model find your core pages, and that can make citations slightly more likely over time.
The best signal of success is not immediate traffic. It is consistency. If you notice your key pages are being cited more often in AI answers, that is a sign the summary is helping. If nothing changes, your core content might still be too vague to cite.
Where it fits in a larger visibility plan
llm.txt should sit below your main content strategy, not above it. The core of AI visibility is still clear pages, strong proof, and a conversion path that makes sense. llm.txt is just a short summary that makes it easier for a model to find those pages.
If you have to choose between rewriting a vague services page or polishing llm.txt, rewrite the services page. The summary only helps if the destination is strong. That is the order of operations that prevents wasted effort.
If you are unsure where to start, review your services page and proof pages first. When those read clearly, the summary almost writes itself.
If you want to see how your pages read in traditional search snippets, the SERP preview tool is a quick way to check. If you want to connect everything to your conversion flow, your project brief and book a free call paths should be obvious and consistent across the site.
If you want help implementing it
If you want a clean llm.txt file and a visibility plan that ties it to the rest of your site, I can help. The fastest way is to book a free call. If you want a structured intake, use project brief. Either way, the goal is the same: make your site easy to cite and easy to act on.
