Introduction: From Search Engines to AI Assistants
For over two decades, search engines like Google have been the gateway to discovering information online. Whether searching for news, how-tos, or product reviews, we’ve grown accustomed to typing keywords into a search bar and scanning a list of relevant pages.
But things are changing.
In recent years, a growing number of users are bypassing traditional search engines in favor of Large Language Models (LLMs) such as ChatGPT, Gemini, Claude, and others. These models can process natural language queries and return coherent, summarized answers—no need to click through pages.
As LLMs become more prominent, a new discipline has emerged: LLMO (Large Language Model Optimization). Just as SEO helps content appear in search engine results, LLMO aims to get your content referenced or cited in the answers generated by AI models.
In this article, we’ll explore what LLMs are, how they work, what LLMO means for digital marketing, and whether traditional SEO strategies still hold relevance in this new landscape.
- What Is an LLM (Large Language Model)?
- What Is LLMO (Large Language Model Optimization)?
- Why Do LLMs Cite Certain Content? Key Characteristics to Consider
- LLMO in Practice: Insights from Ahrefs
- Don’t Lose Sight of the Ultimate Goal: Conversions
- Conclusion: SEO and LLMO Should Work Together, Not Against Each Other
What Is an LLM (Large Language Model)?
Understanding the Technology Behind LLMs
An LLM is an AI system trained on vast datasets and other text-based content. By analyzing patterns in language and meaning, LLMs are able to understand human queries and generate contextually appropriate responses.
LLMs can:
- Answer complex questions
- Summarize long texts
- Translate between languages
- Generate human-like writing
These models are designed to excel at language-based interactions, making them ideal tools for tasks that require understanding and producing natural language. As their capabilities grow, they’re being integrated into more platforms, from customer support bots to productivity apps and research assistants.
“What is an LLM (Large Language Model)? — From NEC’s article (in Japanese)”
Notable Examples of LLM-Based Services
Some of the most prominent LLM-powered tools currently in use include:
Each of these tools is built on the foundation of LLMs and offers a more interactive and intelligent alternative to traditional search. Instead of returning links, they return answers—often synthesized from multiple sources.
The Challenges of LLMs
Despite their impressive capabilities, LLMs are not without flaws. Two significant issues have emerged:
- Hallucination: LLMs sometimes generate information that appears factual but is actually false. This often occurs when the training data is incomplete, biased, or outdated.
- Prompt Injection: Skilled users can manipulate model behavior through specially crafted inputs, sometimes leading the model to reveal hidden features or produce harmful content.
These challenges highlight the need for caution when relying solely on AI-generated information, especially for high-stakes decisions or fact-sensitive topics.
What Is LLMO (Large Language Model Optimization)?
From Ranking Pages to Being Cited by AI
SEO (Search Engine Optimization) has long been the backbone of digital visibility. It involves optimizing a website so that it ranks higher in search engine results for relevant queries. Over time, SEO has become highly sophisticated, with strategies centered around algorithms, backlinks, structured data, and content quality.
Now, with LLMs entering the spotlight, marketers are asking: Can we optimize our content to appear in LLM-generated answers?
This is where LLMO comes into play. It focuses on crafting content in a way that increases the likelihood of being used, referenced, or cited by language models when they respond to user queries.
SEO vs. LLMO: What’s the Difference?
While both SEO and LLMO aim to improve content visibility, they operate in fundamentally different environments.
| Feature | SEO | LLMO |
|---|---|---|
| Core Function | Ranks web pages in search engines | Influences citations in AI-generated answers |
| Output | List of clickable pages | Direct, summarized answers |
| User Behavior | Users browse multiple sites | Users often stop after one response |
| Optimization Goal | Appear at the top of search results | Be included in LLM output |
| History | 20+ years of evolution | Emerging in the last few years |
| Requirements | E-E-A-T, backlinks, keyword usage | Not yet standardized |
LLMO is still in its early stages, and the rules of optimization are not clearly defined. However, parallels to SEO can be drawn—especially in terms of emphasizing trust, structure, and relevance.
Why Do LLMs Cite Certain Content? Key Characteristics to Consider
The Invisible Rules of AI Citations
One of the biggest challenges with LLMO is that, unlike search engine algorithms, the criteria for how LLMs choose which content to cite is largely opaque. Even with SEO, Google never fully discloses its ranking algorithms—largely to prevent abuse. It’s reasonable to expect AI developers to take a similar stance with LLMs.
Still, based on current observations and expert discussions, certain content traits are more likely to be favored by LLMs:
Characteristics of LLM-Friendly Content
- Trustworthiness
- Use primary sources
- Provide author and organizational transparency
- Back up claims with verifiable data
- Structured Formatting
- Use clear heading hierarchies (H1, H2, etc.)
- Implement lists, tables, and FAQ sections
- Provide meta data and schema where possible
- Authoritativeness
- Cite reputable sources
- Include expert contributions
- Demonstrate subject matter depth
Well-structured, reliable content stands a better chance of being used by LLMs—just as it does in traditional SEO. AI systems must parse and interpret text efficiently, so machine readability becomes just as important as human readability.
LLMO in Practice: Insights from Ahrefs
How Experts Are Approaching AI Optimization
Renowned SEO tool provider Ahrefs has recently started covering LLMO as part of its educational content. According to their insights, optimizing for LLMs may involve several key strategies:
LLMO: 10 Ways to Work Your Brand Into AI Answers
- Ensure accurate citation and attribution within your content
- Monitor brand mentions across platforms, including forums and UGC channels like Reddit
- Contribute to Wikipedia or similar structured, authoritative resources
- Create content that directly answers common questions about your niche
- Use feedback from LLM interactions to refine and improve clarity
- Maintain traditional SEO practices, including technical optimization
However, it’s worth noting that most of these recommendations are theoretical at this stage. Unlike SEO, where there’s two decades of data and experimentation, LLMO is a much newer and more volatile frontier.
Don’t Lose Sight of the Ultimate Goal: Conversions
Visibility Alone Is Not Enough
While being referenced by an LLM might increase brand visibility, it doesn’t guarantee business results. Just like SEO, LLMO must ultimately support user actions and conversions.
Potential pitfalls of focusing only on LLM citations include:
- Users may not visit your site at all, having received their answer from the LLM.
- Over-optimization for AI might lead to content that lacks emotional appeal or persuasive power.
- Poor internal structure might fail to guide users to the next step, such as making a purchase or signing up.
Therefore, content strategies must balance LLMO techniques with conversion-focused design and messaging. Ask yourself: What do I want users to do once they reach my site—even if it’s through an LLM?
Conclusion: SEO and LLMO Should Work Together, Not Against Each Other
A Unified Strategy for the New Era of Search
As we look ahead, it’s clear that LLMs will continue to shape how users discover and interact with information. But the role of websites remains crucial. LLMs need quality web content as training data and sources. Without active websites, LLMs would lack the fresh, reliable data they need to stay relevant.
Rather than abandoning SEO in favor of LLMO, marketers and content creators should recognize that:
- SEO and LLMO share overlapping best practices
- Trustworthiness, clarity, and structure benefit both
- Conversions and user engagement are still the ultimate success metrics
In the years ahead, successful digital strategies will merge SEO’s technical foundations with LLMO’s emerging principles. The two aren’t competitors—they’re complementary tools for visibility in a new era of intelligent search.