SEO for LLM: How to Make Your Website Discoverable by AI & LLMs

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Let us address and understand two key concepts:

Traditional SEO

Traditional SEO is the practice of optimizing webpages to rank higher on search engines using keywords, metadata, backlinks, and technical performance. Its goal is to improve visibility in SERPs (Search Engine Result Pages) when users type queries into search engines like Google and Bing.

LLM SEO

LLM SEO is the practice of structuring content so large language models like ChatGPT, Gemini, Claude, etc can easily understand, recall, and reference it in conversational answers. It emphasizes clarity, semantic richness, and strong entity signals instead of traditional ranking factors.

A couple of years ago, developers, marketers and website owners never had to worry about this topic. But all that has changed since the rise and wide adoption of generative AI and large language models (LLMs). Before the introduction of LLMs, web users relied primarily on search engines like Google and Bing to find information on the internet, and these returned just links to webpages. For years, developers and marketers have understood and focused more on this: making their sites searchable and indexable by search engine crawlers like Googlebot. But with AI tools like ChatGPT, Perplexity, and Claude, they don’t just search. They answer in human language — and can occasionally cite links as references. And this is why this matters. Users mostly now ask AI instead of Googling.

So now, it is important for web developers and owners to understand and know how to make their sites and pages “findable” by these AI tools. And that is essentially what we are here for.

Related: If you’re building with Next.js and want implementation-ready patterns, I go much deeper into dynamic sitemaps, JSON-LD, and llms.txt for the App Router in my Next.js LLM SEO Handbook.

But before we proceed, let us take a step back to first understand how search engines differ from LLM-based systems.

How does LLM search work?

RAG (Retrieval-Augmented Generation) is the foundational approach powering most AI search tools.
When you ask AI like ChatGPT, Claude, Perplexity, etc a question, search works by combining two powerful operations: retrieval and reasoning. Unlike traditional search engines, they do not match keywords. Instead, an LLM first interprets your question semantically. This means the model is not just looking at the exact words you typed. It’s trying to understand the meaning behind your question. It is trying to understand intent, context, entities, and relationships.

“Unlike keyword search, which looks for exact word matches, semantic search finds conceptually similar content — even if the exact terms don’t match."

— Open AI on semantic search

Let’s break it down:

Understanding intent, not keywords

Traditional search matches exact words while LLM semantic understanding interprets what you actually want. For e.g., “hypertension guideline update” becomes “latest official hypertension diagnosis or treatment recommendations.” It focuses on intent, not phrasing.

Understanding context and entities

LLMs consider the surrounding conversation and real-world entities.
“What are the side effects?” refers to the drug previously discussed.
“What did Apple announce this week?” is understood as a company-related news query, not fruit, and “this week” means recent information.

Understanding synonyms, relationships and variations

Semantic search recognizes meaning equivalents:
“update” → “latest changes,”
“guideline” → “recommendation,”
“hypertension” → “high blood pressure.”
This lets it find relevant content even when wording differs.

Reformulating the query for better retrieval

After understanding meaning, the LLM generates improved search queries like:
“latest WHO hypertension guideline 2023/2024,”
“recent changes in high blood pressure management,”
“hypertension treatment recommendations site:gov.”
These richer queries only work because the model understands the underlying intent.

It then rewrites your query into multiple optimized forms, firing off parallel searches across the web, databases, or specialized sources. This produces a pool of potentially relevant documents that go far beyond what a single query would surface.

Putting it all together

After gathering results, the system proceeds to aggressively filter for relevance, recency, authority, and uniqueness. Each document is broken into smaller chunks and converted into embeddings (numerical representations of meaning). Using vector similarity search, the system selects the most relevant chunks that match the true intent of your question, and not just your exact words. These chunks are then fed into the LLM alongside your query, allowing the model to read, compare, and synthesize information grounded in actual sources.

The connection between LLM and Traditional search

LLM search relies heavily on semantic understanding, entity recognition, and query reformulation. But it can only apply these abilities to content it can actually retrieve. If your site isn’t indexed by Google, Bing, or another major traditional search partner, the LLM has nothing to semantically interpret, rewrite, or reason over. In other words, even the smartest AI search systems can’t surface content they can’t access. This is why SEO and proper indexing are essential: if search engines can’t see you, LLMs can’t either.

Traditional SEO vs “LLM SEO” (AI‑Search Optimization)

I believe it is important to note that LLM SEO — also called a few other fancy words like GEO (Generative Engine Optimization), AEO (AI Engine Optimization), LLMO (Large Language Model Optimization) — is basically just traditional SEO but going an extra length to optimize content to be found by LLMs and AI search engines.

You cannot build on LLM SEO without understanding and building on traditional SEO first. And most of the time, if you paid enough attention to getting the traditional SEO for your site right, then you have little to nothing extra to do in order to be discovered by AI.

Credit: Vercel

This means the fundamentals of classic SEO remain essential for getting your pages and content discovered in the first place. As explained in Jenny Ouyang’s article SEO for AI: How to Make Your Product Discoverable by LLMs, if your content isn’t indexed by search engines, it’s often invisible to LLMs.

Here are the foundational SEO concepts that LLM SEO builds upon:

1. Crawlability and indexability
Googlebot, Bingbot, and partner crawlers still determine whether your content enters the search index that LLMs borrow from.

2. Clean HTML structure and metadata
Clear titles, meta descriptions, and semantic HTML help both search engines and LLMs interpret your content.

3. Quality, helpful content
Traditional SEO’s “helpful content” guidelines apply strongly in the AI era. LLMs prioritize pages with strong explanations, clear definitions, and well-structured writing.

4. Backlinks and authority signals
Authority still matters. Pages referenced by other reputable websites are more likely to rank in Google and in turn, more likely to be cited by LLMs.

5. Sitemap and robots.txt configuration
Submitting a sitemap to Google and Bing ensures your pages are findable by the search infrastructure that LLMs depend on. Basic robots.txt rules still govern crawler access.

6. Fast page performance
Search engines and AI crawlers both favor pages that load fast. As noted by Vercel, slow, JS-heavy pages risk being partially invisible.

If you have these in place, you are almost good to go.

What are the actionable steps to improve LLM SEO?

Here are in-depth practices and checklists that will guide you to ensure AI and LLMs can find and reference your content easily:

1. Ensure your site is crawlable and indexable

Crawl-ability and index-ability are mostly managed by two files — robots.txt and sitemap.xml.

A robots.txt file is a simple text file placed at the root of a website (e.g https://www.example.com/robots.txt) to tell web crawlers which pages or directories they can or cannot access. It uses rules like User-agent, Disallow, and Allow to guide search engine bots, helping manage crawl budget, reduce server load, and prevent unnecessary crawling of non-public or irrelevant sections. While useful for SEO and site organization, it’s not a security tool and blocked pages can still appear in search results if other sites link to them.
Below is a robots.txt snippet gotten from my personal website:

User-Agent: *
Allow: /
Sitemap: https://coleruche.com/sitemap.xml

Above, I am just letting web crawlers know that I am allowing all user agents/bots (User-Agent parameter) to crawl all web pages on my website (Allow parameter) for any informtion or context. To make it easier for them to know the different pages I have on the website, I am also pointing them to the location of my sitemap.

A sitemap.xml is an XML file that lists all the important pages of a website to help search engines understand its structure and discover content efficiently. It acts like a map of your site, telling crawlers which URLs exist, how often they’re updated, and how important they are relative to other pages. Like a robots.txt, the sitemap file should also be located at the root of the website (e.g https://www.example.com/sitemap.xml)

If you have a dynamic website — like a blog or documentation website — where the content and pages change over time (new pages are added, removed or updated frequently), then it is advisable to generate the sitemap dynamically.

Related: In the Next.js LLM SEO Handbook, I show step‑by‑step how to wire up dynamic sitemap.xml and JSON-LD generation from your content so AI crawlers and search engines can reliably discover every page.

Remember to submit sitemap.xml to both Google Search Console and Bing Webmaster Tools.

2. Use SSR or SSG and avoid CSR-only sites

AI crawlers like ChatGPT and Claude don’t execute JavaScript, and this is important to know because for your content to be discovered by LLMs, it has to be server-rendered.

“Our research with Vercel highlights that AI crawlers, while rapidly scaling, continue to face significant challenges in handling JavaScript and efficiently crawling content. As the adoption of AI-driven web experiences continues to gather pace, brands must ensure that critical information is server-side rendered and that their sites remain well-optimized to sustain visibility in an increasingly diverse search landscape.”

— Ryan Siddle, Managing Director of MERJ

You can use rendering techniques like SSRs and SSGs and avoid React SPAs and CSR by all means, except for non-trivial, supporting content like comments, likes, etc.

To view how crawlers may vie your site, test pages with a simple command:
curl https://yoursite.com
You can also load the page in a browser, open up the browser console and disable JavaScript. Then navigate to the Source tab and reload the page. If no meaningful content shows, AI crawlers won’t see it.

3. Add structured data (JSON-LD)

Structured data is extra, machine-readable information you add to a webpage to help search engines clearly understand what the page is about. It describes key details like the type of content, product information, author, ratings, prices, events, FAQs, etc.

JSON-LD (JavaScript Object Notation for Linked Data) is the most common and recommended format for adding structured data to web pages. It’s placed inside a tag in the page’s HTML and doesn’t affect the visible content, making it easy to manage and update. JSON-LD uses clean, nested key-value pairs to describe entities and their relationships, helping search engines like Google, Bing, and AI search systems better understand context and meaning, which ultimately boosts visibility, relevance, and eligibility for AI search features.

Here is a snippet of a JSON-LD for a blog post with schema @BlogPosting

<script type="application/ld+json">{
  "@context": "https://schema.org",
  "@type": "BlogPosting",
  "headline": "How to Make Your Website Discoverable by AI & LLMs",
  "description": "The articles description goes here",
  "author": {
    "@type": 'Person',
    name: 'Emeruche Ikenna',
  },
}
</script>

Other schema types include Article, FAQPage, Product, Person.Learn more about structured data and JSON-LD.

4. Create clean, semantic HTML

LLMs prefer content that is:

• well-sectioned with <h1>, <h2>, <h3> with good heirarchy

• logically grouped into topic clusters and avoid large walls of text

• use bullet points and numbered listings

• readable even without CSS or JS

• dense with meaning, low on fluff

Clear structure improves semantic similarity retrieval, the core of RAG systems.

5. Write concise summaries at the top of your pages

LLMs rely heavily on:

• opening paragraphs

• abstract-like summaries

• definitions and context at the top

These sections become the “embedding anchor” that determines how your content is recalled.

6. Build internal linking across related topics

Internal links help LLMs understand category structure, topic relationships, and conceptual clusters. This increases your retrieval score during AI search.

For example, you should have a “related articles” sections that link to other articles that may be similar, having hyperlinked texts in an article that references another article on your blog, or having interlinked blog series.

7. Add an llms.txt file (emerging standard)

An llms.txt file is an emerging, unofficial proposal that aims to give website owners a way to signal how they prefer their content to be used by AI models.

It is similar in spirit to robots.txt, but focused on LLM training, indexing, and dataset inclusion. Although the idea has gained attention after early discussions from projects like llm-api and posts from Hacker News and The Verge examining AI data governance, there is still significant debate about its real-world effectiveness, since no major AI company currently treats it as a binding standard. Regardless, adding an the file carries no downside: it doesn’t interfere with normal SEO, it’s easy to implement, and it publicly documents your preferences around AI usage in a transparent, machine-readable way.

While the ecosystem evolves, llms.txt is seen as a “no harm, no foul” option for creators who want to stake out clear expectations for how their content should be consumed by LLMs.

Here is a sample snippet:

site_title: Your Site Name
site_description: A concise description of your website's purpose.

allow: openai
allow: perplexity

disallow_training: all
allow_snippets: all

require_attribution: true

Learn more about the llms.txt proposal.

8. Optimize performance and time-to-first-byte

AI crawlers have much shorter timeouts than browsers. If your server responds slowly, they simply abandon the request, meaning parts of your site may never get indexed.

Improve by focusing on:

• caching (CDN, edge functions)

• HTML delivery speed

• image compression

• code-splitting (without harming SSR)

Fast backends lead to more complete crawls.

9. Maintain content freshness

LLMs prioritize content that remains current and valuable over time. This means regularly updating pages with new insights or corrections, incorporating the latest statistics, facts, and data points, and maintaining evergreen articles that are periodically refreshed with relevant examples. Consistently fresh content enhances both indexing frequency and retrieval accuracy.

10. Get external citations and backlinks

AI-powered systems prioritize content that demonstrates both popularity and relevance, much like traditional SEO. To build this authority involves creating meaningful connections. These connections can be between related pages on your own site (as described in 6. Build internal linking across related topics) and gaining references from niche directories, forums, and specialized blogs. Additionally, promoting your content through newsletters, social media, and backlinks from trusted sources amplifies its reach and credibility. While AI may evaluate these signals differently than Google, the underlying principle of trust and authority remains consistent across platforms.

11. Test your site in AI models regularly

Ask Perplexity or ChatGPT with browsing enabled to analyze your website by requesting a summary of its content. Ask, specifically, what the page contains, and verifying whether it loads correctly. This process helps ensure that AI models can access and interpret your site’s information accurately, highlighting any potential issues with visibility or rendering. If the model is unable to see the content, it indicates a problem that needs to be addressed, such as server-side rendering, crawlability, or indexing issues. Regular testing with AI tools provides valuable feedback to maintain your site’s accessibility and discoverability.

In conclusion

AI-powered search and LLM SEO are transforming how websites and contents are discovered and referenced. Get traditional SEO fundamentals right (crawlability, clean HTML, quality content, and authority), and by building on it, developers can ensure their content remains visible to both search engines and AI systems.

LLM SEO adds an extra layer, emphasizing structured data, server-side rendering, internal linking, and emerging standards like llm.txt to maximize discoverability. Regular testing with AI tools and maintaining content freshness further strengthen a site’s accessibility in the evolving AI landscape. Integrating these strategies ensures that your content not only reaches a wider audience but also becomes a trusted source for AI-powered answers, keeping your website relevant and authoritative in the age of generative AI.