To preserve influence, brands need to build websites that actually get cited and summarized by AI. Building an LLM-friendly website involves having the right digital infrastructures that AI models can parse — semantic HTML, structured headings, and structured data. Beyond that, the content on the page directly answers search queries in clear, concise language.
This article curates best practices and industry experiments to explain what an LLM-friendly website is and how to build one.
Let’s start with the basics.
Table of Contents
- What is an LLM-friendly website and why does it matter?
- Do you need an llms.txt file?
- How to create an LLM-friendly website.
- How to Implement the llms.txt File
- Frequently asked questions about LLM-friendly websites.
What is an LLM-friendly website and why does it matter?
An LLM-friendly website provides understandable, extractable content that AI models can parse. Information gets reused when ChatGPT, Gemini, Perplexity, and other AI search systems generate answers to user queries.
To unpack what makes a website LLM-friendly, let’s figure out how LLMs work.
Large language models are AI engines trained on massive amounts of data to predict and generate human-like language. They analyze relationships — connections, patterns, or contradictions — to develop a unique, comprehensive conclusion. This logic makes website content clarity, structure, and authoritativeness imperative.
For SEO specialists and marketers, LLM-friendly websites must include:
- Clear self-contained topical sections with direct answers.
- Semantic HTML and schema that communicate meaning.
- Entity-rich content that signals authority on key concepts and relationships.
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Why does LLM friendliness matter today?
LLM website optimization makes brands more likely to appear in AI search, an increasingly important marketing and sales channel. Take Sure Oak Agency. They have attributed 40% of new leads to AI-driven discovery, after increasing ChatGPT referrals by 41% and AI Overview visibility by 286%.
Furthermore, LLM-optimised content attracts more relevant consumers. Of marketers, 58% note that AI referral traffic has much higher intent than traditional search, according to the 2026 HubSpot State of Marketing report.
So, LLM visibility is worth investing in. But before taking action, marketers should establish a baseline for how AI platforms currently perceive, reference, and position their websites.
Use the free HubSpot AEO Grader to see how frequently the brand is mentioned in AI search compared to the competition. Marketers can also assess the sentiment of their AI mentions and which parts of the brand’s offering are highlighted — or completely overlooked.
What I like most is how AEO Grader analyses market growth opportunities. It assesses your brand based on sentiment analysis, customer feedback, and industry benchmarks.
For HubSpot, it suggested strengthening its presence in the enterprise sector — and we definitely agree on that.
Watch a 3-minute video AEO Grader tutorial, and get your website LLM-optimized.
Do you need an llms.txt file?
An llms.txt file can be helpful, but it’s not mandatory. These plain text files point LLMs to the most up-to-date and helpful content for AI search, without additional code that clutters essential information. Adoption of llms.txt files is still growing, and AI search companies have yet to confirm that their models crawl these files.
Opinions on its value cause heated debates. Before weighing those arguments, let’s first clarify what llms.txt is.
What is llms.txt?
llms.txt is a plain-text file in a website’s root directory that provides a curated overview of the website’s content for LLMs. Introduced in 2024 by an Australian data scientist, Jeremy Howard, llms.txt complements the sitemap and robots.txt standards.
While a sitemap lists all pages for search engines and a robot.txt file outlines what is allowed and denied for indexing, llms.txt explicitly guides AI systems to the most authoritative, up-to-date content.
Here is an llms.txt example from the ListDefender that lists essential and optional content along with navigation links:
Arguments For and Against llms.txt
The overall adoption of llms.txt is relatively niche. According to NerdyData, only about 4,118 domains have published an llms.txt file. This is just a drop in the ocean, given how many websites are out there.
So today, llms.txt is currently a recommendation rather than a directive by the major AI companies. In fact, no LLM companies have officially announced that it’s obligatory when crawling websites.
In addition, the audit of CDN logs for 1,000 Adobe Experience Manager domains conducted by Flavio Longato, GEO Strategist at Adobe, showed that almost every AI Crawler ignores them.
Semrush didn’t see any effect either: The llms.txt page they analyzed received zero visits from the Google-Extended bot (Google’s AI crawler), GPTbot (OpenAI's crawler), PerplexityBot, or ClaudeBot.
At HubSpot, we've done a few deep dives into our web traffic data, and as of now, we’re not seeing enough evidence that answer engines (ChatGPT, Claude, Perplexity) are using llms.txt as a convention for crawling web data.
On the other hand, supporters of the llms.txt standard argue that modern websites are increasingly difficult for AI systems to read. Much of the content is hidden behind JavaScript, buried in design layers and complex site structures. From that perspective, llms.txt provides direction on the most important info to pay attention to.
So, do webmasters need an llms.txt file?
“This really depends on the difficulty of implementing the llms.txt file. If you feel that it would be relatively easy to create the file, then go for it. If it requires a large amount of resources, then I’d recommend you hold back until we clearly see benefits,” suggested Flavio Longato upon completing his audit of CDN logs.
Among big players, though, Hootsuite has already implemented llms.txt.
Pro tip: Watch the signals of llms.txt becoming obligatory. Monitor documentation updates and policy revisions of GPTBot, Google-Extended, ClaudeBot, and the Meta crawler. If llms.txt is not the business of the day, it can become a directive at any time.
How to create an LLM-friendly website.
Currently, LLMs give priority to the sites that crossed out the five optimization checkmarks:
- Authoritativeness and proofs. Generative engines favor content with verifiable sources and clear author bylines.
- Multiform evidence. Text, images, data charts, video, and tables increase the likelihood that the content will get to AI answers.
- Structured, snippet-friendly markup. FAQs, HowTos, Product, Offer, Breadcrumb, and Dataset schema make content machine-readable and more likely to be surfaced.
- Deep topical coverage. The more comprehensive the content, the more understandable the context for AI engines.
- Freshness. Regular updates and clear “last updated” metadata improve trust.
As Michael King, Founder of iPullRank, puts it: “Generative AI isn’t a technical problem; it’s your content strategy that determines how well you’re surfaced.”
From this, let’s learn how to build an LLM-friendly website.
1. Organize a clear and predictable page hierarchy.
Organize content into logical sections using H1–H4 headings that reflect how users ask questions. A logical hierarchy helps LLMs quickly identify primary topics, subtopics, and relationships between ideas, improving extractability and citation accuracy.
Opt for 2-3 line paragraphs, each one conveying one useful insight, so it’s a complete thought.
We follow what we preach, so this article can make for a good illustration of well-structured content. Skim it again to analyze the headings hierarchy and relationships between ideas.
2. Start sections with direct answers.
Begin each section with a short, explicit answer or summary. LLMs favor content that surfaces conclusions upfront, making it easier to quote without inferring intent from long paragraphs.
To reinforce direct answers and provide context for LLMs, opt for semantic triples — Subject-Predicate-Object (SPO) statements. They define relationships between entities (e.g., “Apple” [Subject] — “produces” [Predicate] — “iPhones” [Object]), enabling AI to understand context rather than just keywords.
3. Include specific examples and experience.
Abstract explanations are harder for AI systems to reuse. Specific examples, numbers, scenarios, and use cases add clarity and reduce ambiguity.
Unlike generic statements, case studies, professional anecdotes, practical tips, and lifehacks strengthen authority and expertise, increasing the chances of being cited.
The biggest win is to add your own data to the page and become an original source that AI will be quoting. At HubSpot, we sprinkle unique data across landing pages, too. Not only does it help with conversions, but AI systems also pick it up, providing users with a link to our main landing page.
4. Provide context through term definition.
Explain key terms directly on the page, so LLMs don’t need to look elsewhere to understand important information. For example, instead of assuming that AI knows what “schema markup” means, briefly define it as a special code that improves the way search engines understand content. When definitions live next to the concept, LLMs quote the content accurately without losing context.
5. Add summaries, bullets, tables, FAQs, TOCs, and key takeaways
LLMs often extract content from summaries and structured blocks. Section recaps, tables, FAQ modules, lists, table of contents, and Key Takeaways surface the most reusable information and improve visibility in AI Overviews and generative answers.
At a startup, my first experiment was to upgrade 10 top-performing articles with Key Takeaways and the bottom FAQ sections. Within a month, I saw skyrocketing results: the 8x increase in ChatGPT citations, achieved using only these changes.
Since writing comprehensive articles is very time- and effort-intensive, I recommend HubSpot’s AI Content Writer — the tool helps content teams create structured, LLM-friendly content faster, directly inside the CMS.
It generates SEO suggestions for teams that align with AEO requirements, such as logical sections, FAQs, and summaries — the structures AI loves.
Case in point: Thanks to HubSpot AI Content Writer, Franchise Brokers Association (FBA) creates 2,000-word articles daily, boosting organic leads by 216% and closing 60% more deals.
6. Plan content updated every three to six months.
Not a technical tip, but keeping content fresh is arguably one of the most important things for AEO. Update content every three to six months by adding more internal links, new quotes, or new paragraphs.
7. Use clean, semantic HTML.
LLMs primarily parse raw HTML. Proper heading tags, lists, tables, and alt text make meaning explicit and reduce reliance on fragile client-side rendering.
Content that depends on complex JavaScript frameworks can be partially invisible to AI crawlers. Lightweight rendering improves accessibility, reliability, and long-term maintainability.
Don’t rely on frameworks that render most of the content client-side, like certain single-page apps or heavy React implementations.
Instead, write clean HTML output that delivers the key content immediately. Use JavaScript for interactive elements only, while keeping the main text, headings, and key metadata fully accessible in the page source.
The image below clearly illustrates how AI reads HTML and heavy JavaScript.
9. Implement rich snippets wherever relevant.
Rich snippets are visually appealing Google search results that display extra data — such as ratings, prices, images, and widgets — beyond the standard title and description. Webmasters generate snippets using Schema.org markup and add them as structured code to a website’s HTML to help search engines understand the context.
LLMs like ChatGPT also use snippets to line up product recommendations inside their answers, as snippets clarify content intent.
Pro tip: Start by adding FAQ and HowTo schema to key pages. Follow the Google guidance, then test in Google’s Rich Results Test to ensure AI can read and surface them correctly.
10. Keep the site fast and lightweight.
Fast-loading pages reduce fetch latency and failure rates for AI scrapers and crawler-based retrieval systems. Performance directly affects how often content gets fetched, reprocessed, and reused at scale.
Webmasters have to ensure the site loads under two seconds to appear in LLMs. Follow these key technical steps for faster load times:
- Low TTFB and stable rendering: Optimize server response times, use edge/CDN caching, and avoid render-blocking resources. Use Google Lighthouse (TTFB), WebPageTest, or server access logs. Target: sub-500 ms TTFB on primary templates.
- Minimal JavaScript dependency: Ensure core content renders without client-side execution in Chrome DevTools Coverage. Prefer static or server-rendered HTML.
- Clean CSS and DOM structure: Reduce unused CSS, avoid deeply nested DOM trees, and limit layout shifts. Check the site in WebPageTest.
- Efficient hosting and caching: Use modern hosting with HTTP/2 or HTTP/3, proper cache headers, and compression (Brotli or gzip).
Also, avoid heavy scripts or trackers that load conditionally and create fetch inconsistencies.
Bottom line: LLMs favor pages that load predictably, expose content immediately, and do not require complex execution to extract meaning.
11. Maintain crawl hygiene.
Crawl hygiene ensures that both search engines and LLM retrieval systems can reliably discover, interpret, and prioritize content. Check whether the site follows core crawl practices, including the following.
- Clear URL structure: Human-readable, stable URLs with logical hierarchies. Use Screaming Frog or Ahrefs Site Audit to catch errors.
- Canonicalization: Ensure one canonical per content entity to avoid citation ambiguity.
- Accurate sitemaps: Up-to-date XML sitemaps that reflect indexable pages only. Use sitemaps validators to check.
- Well-scoped robots.txt: Allow access to content-bearing pages while blocking low-value or private endpoints.
- Structured internal linking: Implement breadcrumbs and use contextual links inside the site’s content that reinforce topical relationships.
- Consistent metadata: Ensure clean meta titles and descriptions aligned with page intent.
Whether the team is building a website from scratch or optimizing the existing one, keep in mind that early brand mentions and baseline visibility signals typically appear within two to six weeks. More stable citations and ranking improvements across AI platforms usually take three to six months, depending on crawl frequency and site depth.
Featured resources:
- 12 Ways to Create a User-Friendly Website Registration Process
- Webpage vs. Website: Differences You Need to Know
How to Implement the llms.txt File
Follow this straightforward process to create the llms.txt file.
1. Choose the most important web pages.
Before creating the file, determine which pages or sections are most central to the brand’s value and expertise. Prioritize content that clearly represents the business, such as product/service pages, key blog posts, documentation, and FAQs.
This file may direct AI systems to the most meaningful pages.
2. Create a file in Markdown.
Create a new file named llms.txt. in a Notepad or Visual Studio Code. Start describing the website's central content using Markdown. Basic Markdown elements include:
- # for the H1 heading
- > for a short description
- - or * for lists
- [text](url) for links with optional descriptive text
There are no strict rules for describing the website content, so the wording and hierarchy are totally up to each person. Still, here is a template to start with:
# Website Name
> A concise description of what the site offers
## Core Resources
- [Primary Guide](https://example.com/guide): Overview of key use cases
- [Product Page](https://example.com/product): Main product features
## Documentation
- [API Docs](https://example.com/docs/api): Technical reference and examples
## Optional
- [Blog](https://example.com/blog): Latest insights and updates
3. Place llms.txt at the website root.
Once the file is ready, upload it to the root of the domain on the server, neighboring with robots.txt and read.me.
For example, in the VSCode editor, the llms.txt file will be nested at https://yourdomain.com/llms.txt:
If you want the file to cover only a specific section (such as documentation), it can live in that subdirectory (e.g., /docs/llms.txt). Consistency in location makes discovery predictable.
4. Add link descriptions.
When listing URLs, always include brief, informative descriptions next to links. The link descriptions provide meaningful context for the linked page, potentially helping LLMs understand why the resource matters without visiting it.
Avoid generic text like “click here” — self-check the link description against filler words and make sure each word conveys meaning and can’t be removed.
5. Test and validate the file.
After deployment, verify the file’s accessibility in a browser. Look for clean Markdown output. Web teams can also use the llms.txt generators or validators to check structure and syntax before publishing.
These two llms.txt generators return well-structured files:
6. Maintain the file over time
Like any curated resource, llms.txt requires updates. As the team adds, removes, or updates site content, revise the file to keep it relevant. Remove outdated URLs and add new pages that reflect the latest business priorities.
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Frequently asked questions about LLM-friendly websites.
Is llms.txt the same as llm.txt?
No — llms.txt and LLM.txt are not the same, and only llms.txt is a proposed convention. The plural form (llms.txt) is intentional, mirroring robots.txt, and is what most discussions refer to when discussing LLM guidance files.
That said, there is no official standard yet, so crawlers won’t magically infer intent if teams use the wrong filename. But for future compatibility, llms.txt is a safer option.
Is exposing transcripts or HTML fallbacks considered cloaking?
Generally, no, if the content is equivalent. Providing transcripts for videos, HTML fallbacks for JS-heavy pages, or simplified views for bots is accessibility- and crawlability-friendly.
It becomes cloaking only if teams intentionally show materially different content to crawlers than to humans (e.g., keyword-stuffed text that bots see but humans don’t). If the fallback is a faithful representation of the user experience, the page is in the clear for both search engines and AI crawlers.
Do I need to switch to a static site to be LLM-friendly?
No, teams do not need to switch to a static site to be LLM-friendly. Dynamic frameworks (Next.js, Nuxt, SvelteKit, etc.) can work perfectly well. What matters is that content is reliably renderable, indexable, and accessible without brittle client-side execution.
That said, pre-rendering, SSR, or hybrid approaches help a lot. They reduce crawl friction, improve extraction accuracy, and lower the chance that an LLM crawler skips or misreads content. Think “predictable HTML,” not “static-only.”
How do I test robots.txt rules for AI crawlers?
Start by manually reviewing robots.txt for known AI user agents (e.g., GPTBot, Google-Extended, ClaudeBot). Make sure rules are explicit, not inherited accidentally, and that important assets like CSS or text endpoints are not blocked.
Then, check server logs to see whether AI crawlers are hitting blocked paths, and adjust rules accordingly. Unlike Googlebot, many AI crawlers are inconsistent in how strictly they follow robots.txt, so testing is observational rather than guaranteed.
Can I block AI models and still rank well?
Yes. Blocking AI crawlers doesn’t directly harm traditional search rankings because Google, Bing, and other search engines use separate crawlers. Rankings depend on search bots, not LLMs’ training bots.
The tradeoff is visibility in AI-generated answers and citations. Blocking AI models may reduce how often content is summarized or referenced by chat-based tools — but if the priority is organic search traffic or IP protection, that’s a perfectly valid choice.
Boost your LLM visibility with an LLM-friendly website.
Nowadays, an LLM-friendly website is a must. Brands now need to be understood, trusted, and cited by LLMs to capture prospects’ attention as clicks shift to AI-generated answers.
From my experience with AEO optimization, the biggest wins come from optimized content structure and technical setup. For sure, SEO experts can try llms.txt as an experiment, but it shouldn’t replace the main optimization framework.
To improve the site’s visibility in LLMs, audit it in HubSpot’s free AEO Grader. It shows how AI perceives the brand now, what it cites, and which areas need improvement.
From there, make regular audits and see more customers coming from LLMs.
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