How to Make Your Content LLM-Ready for Better AI Search Ranking

Traditional Google search now has a potent adversary in information search, large language models (LLMs), also referred to as generative AI. How do these tools change content marketing, and what marketers should do to make their content LLM-ready?

This guide will touch upon the importance of AI search and provide the best practices of LLM optimization.

Why “LLM-Ready” Content Matters Now

Content marketing remains a valid approach to generating traffic from social media and organic search. Since the AI search engines have appeared and surged in popularity, though, the dynamics of content marketing have shifted to include these tools in content distribution.

Even though LLMs haven’t replaced Google entirely as a way to find information online, they’re vastly popular. ChatGPT alone reports having 700 million weekly users, with other tools slightly behind. Google’s inclusion of AI Overviews and AI Mode into organic search also adds an element of LLMs to the standard search experience.

Not optimizing your content for LLM search means you’ll lose potential traffic from LLM tools, and organic positions on Google will be moved down by AIO.

Several optimization techniques have emerged for making content LLM-ready. Despite referring to the same set of practices, these techniques are known as:

• LLMO (LLM Optimization).
• GEO (Generative Engine Optimization).
• AEO (Answer Engine Optimization).
• LLM SEO.
• AI SEO.

What Does “LLM-Ready” Actually Mean?

LLM-ready content is content optimized for LLMs. Simply put, this means that the content can be:

• Understood by the LLMs.
• Interpreted and summarized by them.
• Used in AI search results.
• Linked to in the answers.

This is similar in some ways to content optimization for search engines, but also quite different in others. Here’s a quick breakdown of the differences between the two.

Both approaches benefit from high-quality content and properly used meta-tags and schema markup. But optimization for AI search results focuses more on structuring content to be well understood by LLMs in its entirety rather than adding a few keywords and phrases.

How AI Search and LLMs Interpret Content

Large language models interpret texts through a process called tokenization. They break down the input phrase into words and phrases and then interpret their importance and make sense of the whole text. You can see a visualization of how this works in OpenAI’s Tokenizer.

In practice, related to content optimization, this process looks like this:

• The LLM retrieves content related to the tokens in user input.
• The LLM searches through content for the most relevant information.
• It either finds a relevant subtopic it can quote or rephrase, or finds importance markers like “key points,” or “the most important thing.”
• It provides a summary of relevant points to the user with a link to the source.

The most important difference between traditional and AI search is that organic Google search uses keywords and other signals to understand whether content is relevant to the search, leaving it to the user to read the text and find the most relevant information.

LLMs can understand the text in its entirety and present the user with the most relevant points.

Measuring and Improving Your AI Search Visibility

Generative engine optimization is useless without a way to measure the results because improvement becomes impossible. Tracking AI visibility can be tricky, however.

To do this, you’ll need to develop a list of queries you want to track and run them through several AI tools on a schedule. You can track short user queries because you can’t know every possible question relevant to your content, and LLMs will break down larger questions into smaller ones anyway.

There are plenty of tools that do this for you on the market. A good AI visibility tracker scrapes AI search results from a browser instead of using an API, as this provides more contextual details in the response and mimics the way users experience these tools.

As there’s no way to track clicks in this way, you should also measure referral traffic from LLM websites. Use GA4 to track referral traffic from AI websites when they provide a link to your content.

It’s an imperfect solution because it can’t attribute traffic to a specific user query, but it provides a general understanding of how positions on LLM correlate with traffic.

Structural Best Practices to Improve AI Visibility

AI search optimization is focused primarily on structuring the content in a way that’s easy for LLMs to understand and writing it well. Even without additional efforts, a piece of content that covers all major questions and subtopics associated with it in a clear way will be able to be quoted by AI tools.

But the content itself is not the only thing that influences how often it gets quoted on LLMs. There are other factors at play, for instance, Core Web Vitals. Pages that load faster can get quoted up to three times more frequently. Use audit tools like SEO Checker by SE Ranking to check those factors and improve them. The tool can also provide text readability ratings for your published content.

Content Depth: How to Be Useful Enough for AI Answers

Creating content that covers the topic deeply and from multiple angles serves both the end user and the AI search engines, but does so differently. Understanding this slight difference is key to seeing the logic behind LLM optimization best practices.

For the reader, a piece of in-depth content is often a text they will read in its entirety to understand every aspect of the topic.

For an LLM, a text is more like a collection of information points that it can draw from to present the user with the most relevant ones. People rarely need to receive 2,000 words of the original text to answer their narrow question, so AI will either summarise the whole text or retrieve the most relevant parts.

That’s why to optimize your content for LLMs, you need to structure it as a series of self-contained subtopics. Here are a few best practices for doing that.

• Create a logical structure and follow it, making each section self-contained.
• Use common user questions about each subtopic as elements of the structure, or answer them in the text in another way.
• Make your content easy to navigate with headings and semantic markers.
• Avoid adding irrelevant text, write briefly and to the point.
• Repeat core ideas throughout the text.
• For each section, create a short paragraph that answers the core idea briefly.
• Add summaries, bullet point lists, and tables to the text.

The most important thing is to pool all relevant questions and subtopics into your text and answer them in a clear manner.

Another thing you should keep in mind is that AI results typically prioritize well-researched content. Emphasize that by either linking to research you base your opinions on or mentioning it in the text.

Using Semantic Signals and Metadata Effectively

Giving LLMs the ability to find the right information in your content is key to accurate retrieval and quotation. Here are a few best practices for that.

• Fully reveal the main topic of the text in the meta title and meta description.
• Use a logical structure with headings that explain the content in them well.
• Use potential user questions as headings.
• Use context signals like “this is the most important thing” or structuring a subtopic in a series of steps.
• Create brief summaries and place them closer to the beginning of the text.

Following these tips on LLM optimization will help the AI to find the right information in your content and serve it to end users.

Write for Humans First — While Staying AI-Friendly

The best part about LLMO is that LLM-friendly content is also a human-friendly one. Unlike old SEO practices that rewarded people for creating keyword-stuffed content, the first practices of LLMO focus on:

• Logical coherence.
• Readability.
• Ease of finding information.
• Clarity.
• Covering a topic in detail.

All of these also make content great for the end-user, not only for the LMM. If you’re having doubts about adding detail that might not be optimized for LLMs, always stay on the side of your reader. With proper structure and other optimization elements in place, your stylistic choices won’t prevent content from appearing on LLM tools.

Quick Checklist: Making Your Content LLM-Ready

• Meta tags describe the main topic of the text.
• All important subtopics are included in it.
• There’s a clear logical heading structure.
• Headings describe the content they contain.
• Content in headings is self-contained and readable.
• Summary provided at the beginning of the text.
• Contextual cues are used for the most important information.
• Recency is shown by either a “last updated” timestamp or with Schema markup.
• Author information is included.
• Key points are reinforced by linking to research.

Summary

LLM-ready content allows your brand to have more visibility on AI search engines and receive more traffic from them. What’s even more important, optimizing content for LLMs doesn’t require you to make it worse for the reader. Instead, it’s even better for the as scannability and readability are core pillars of LLM content optimization.

Use the current best practices, learn new trends in LLM optimization, and use a combination of an AI results tracker and GSC to monitor your successes with LLMs.