The rapid rise of AI has transformed the way we create content. However, its effects go beyond the use of generative AI tools to brainstorm or create. AI is also reshaping the way we present our content to the world as AI optimization is an increasingly important component of discoverability. This article is the first in a series called “Mastering LLM Optimization: The Future of Content Visibility” that will help creators adapt to this new reality.
The search landscape has undergone a seismic shift. While markers and creators have spent decades perfecting their SEO strategies for traditional search engines, a new paradigm is rapidly emerging—one where AI-powered interfaces don't just rank content, they synthesize and serve it directly to users. This transformation demands a fundamental rethinking of how we approach our content.
What is LLMO and why it's growing 1,200% year-over-year
Large Language Model Optimization (LLMO) is structuring digital content to ensure it can be accurately understood, extracted, and reused by AI systems like Google AI Overview, ChatGPT, and voice-based search. Unlike traditional Search Engine Optimization (SEO), which focuses on page rankings, LLMO emphasizes semantic clarity, structure, and machine readability—optimizing for how AI systems comprehend content, not just where it ranks.
The market impact is substantial: generative AI traffic has grown by 1,200% between July 2024 and February 2025, while companies implementing LLMO see an average of 60% increase in AI visibility within 6 months. Primary platforms include ChatGPT (600M users), Google AI Overviews (1.5B users), Claude, and Perplexity.
The fundamental difference lies in the goal. Traditional SEO is all about getting the top spot whereas LLMO fights to be one of the two or three sources AI systems actually recommend. There's no second page in AI responses.
"Entity Optimization: Teaching AI what your content means
The transition from traditional SEO to AI-optimized content represents more than just tactical adjustments. It requires a complete philosophical shift in how we approach content discovery.
But the real shift goes deeper than query formatting. AI systems think in entities rather than keywords. Traditional SEO optimizes for text strings like 'Italian restaurant NYC.’ However, AI understands entities and the connections between them, like how 'Mario's Restaurant' (business) is connected to 'Manhattan' (location) and 'Italian cuisine' (category). The ability to recognize entities is why AI knows that 'Apple' means the company when you ask about iPhones but the fruit when you ask about pie recipes.
Consequently, you should use schema markup to identify people, places, and things in your content. Create clear relationships between concepts so AI can build a knowledge graph that connects the entities in your content to the vast web of information it already uses to answer questions. Instead of keyword stuffing, you should build topic authority by thoroughly covering all aspects of your core entities and their relationships. When AI sees clear connections like "Tesla → manufactures → Model 3 → uses → lithium batteries," it can accurately place your content in its knowledge graph and draw on it for relevant queries. This is why Answer Engine Optimization works. It's built on entity relationships that are mappable and navigable by AI.
Answer engine optimization (AEO) is a subset of LLMO that focuses on crafting content so AIs can display it in response to user queries. AEO differs from SEO because it focuses on answer engines and conversational questions while SEO targets search engines and keywords.
Here are some ways that AEO and SEO differ in practice:
Query processing: AEO targets conversational and voice-based searches, while SEO targets text-based queries on traditional search engines. Instead of optimizing for "best dog food," you now optimize for "What's the best dog food for a senior dog?"
Content structure: AEO emphasizes short, structured formats like FAQs, featured snippets, and schema markup. Conversely, SEO favors long-form, keyword-rich content. AEO content is designed to be scannable and precise while SEO content is generally longer and more detailed.
Real-world impact: How AI Chat interfaces are altering discovery
The implications of this shift extend far beyond search rankings. AI-driven interfaces are fundamentally changing how consumers discover and interact with brands across industries.
The biggest trend is AI agents managing entire shopping workflows independently. They can research products, compare options, and make purchases based on user preferences—all on their own. This will only become more and more common. Instead of browsing endless product pages, customers will simply say "find me the best laptop under $800" and trust their AI to handle everything else.
As AI agents handle more purchases, sellers must optimize product listings for machines. As a result, platforms may need to rethink rankings, ads, and marketplace design. The traditional e-commerce funnel is being compressed as customers are increasingly guided by AI.
Where traditional SEO falls short in AI environments
The shift to AI-driven search exposes critical weaknesses in conventional optimization approaches. Content that ranks well in traditional search often fails to gain traction in AI environments due to fundamental structural and strategic differences.
Format failures that cause AI to skip your content
Long continuous text without subheadings is difficult for LLMs to process, and websites with that type of content are likely to be given less attention by LLMs. However, content that provides a holistic, contextually accurate response to user questions will do much better.
Five quick wins: Immediate changes for AI visibility
Traditional SEO encourages us to take a holistic, page-focused approach. AEO, on the other hand, requires a more granular approach. Here are some best practices to keep in mind:
- Break your content into question and answer blocks.
- Make sure your headers reflect real search queries (e.g., “What’s the best food processor for under $100?”).
- Provide answers quickly.
- Add structured data to help LLMs find pertinent information such as FAQs, how-tos, etc.
- Signpost summaries with labels such as “TL;DR”.
For advice on how to do these things, check out this post by Josh Markus of Inbound Design Partners.
The path forward: Preparing for AI-first content strategy
AI-driven discovery isn't some technofuturist daydream. Today, roughly 60% of searches end without any visits to a website as users find what they need on the search results page itself. Publishers can no longer rely on rankings alone. Being visible now means earning a top spot in the AI-generated answers. By making your content understandable to AI, you can bolster your chances of landing that coveted spot.
Up next in our series: Learn the specific techniques for structuring your content for AI comprehension.
About the author,
José Mauricio Duque
