LLMO / GEO: How to optimize content for LLMs and generative AI like AIOverviews, ChatGPT, Perplexity …?
In the rapidly evolving digital landscape in the AI era, a silent revolution has fundamentally transformed how users discover information online. While SEO professionals once focused exclusively on securing the coveted top spots in Google’s search rankings, today’s reality demands a more sophisticated approach.
Large Language Model Optimization (LLMO), also known as Generative Engine Optimization (GEO), represents the next evolutionary leap in digital visibility—one that’s already reshaping how brands connect with audiences.
As AI-powered platforms like ChatGPT, Google’s AI Overviews, and Perplexity increasingly mediate the relationship between users and information, traditional SEO tactics alone no longer secure digital visibility.
The shift will come: Gartner predicts traditional search volume will decline by 25% by 2026, with organic traffic potentially dropping by over 50% as consumers embrace conversational AI interfaces. In my opinion 2026 is too early for this prediction, but I think till 2030 this could be reality.
This isn’t merely a technical adjustment but a paradigm shift in how content must be conceptualized, created, and distributed. Unlike traditional search engines that return lists of blue links, these new AI intermediaries synthesize information into comprehensive, direct answers—often without attributing sources or driving clicks to websites.
The game has changed from ranking individual pages to ensuring your brand becomes part of the conversation itself. For marketers and content creators, this transformation represents both challenge and opportunity.
Success now hinges on understanding how LLMs process, evaluate, and reference information. It requires content that seamlessly balances human engagement with machine comprehension, structured to be not just findable but extractable and contextually relevant. As we navigate this new frontier, those who master the art and science of LLMO/GEO won’t just maintain visibility—they’ll thrive in an AI-mediated future where being mentioned matters more than being clicked, and where authority is built through recognition rather than backlinks alone.
This article should uncover a bunch of approaches for optimizing content for LLMO / GEO I researched out of several research papers and patents about LLMs especially Retrieval Augmented Generation (RAG) and Grounding. For this purpose, I analyzed all documents related to LLMO in my SEO Research Suite database with the help of the LLMO / GEO AI Assistant, among other things, to find approaches on how to optimize content in order to get more links / references in the answers of generative AI.
Retrieval Augmented Generation (RAG) and Its Impact on Content Visibility
RAG is a technique in artificial intelligence that combines two main components:
- Information Retrieval : The system searches external databases to find relevant information
- Generative Language Models : The retrieved information is then fed as context into the model, which generates a response.
RAG follows a two-step process:
- Retrieval : First, a search query is made to an external database to find relevant information. This database can include collections of texts, structured data, or knowledge graphs.
- Augmentation : The retrieved information is then fed as context into the generative model, which generates a detailed and informed response based on both its pre-trained knowledge and the retrieved information.
RAG significantly changes how content gets surfaced in AI responses:
Source Prioritization
RAG systems typically prioritize content from:
- Timely and authoritative news websites
- Reputable industry publications
- Established knowledge platforms
- Discussion forums
- Knowledge graphs
- Sources that rank well in the respective underlying retrieval system (Google, bing …)
- Content that is easy to understand and process by LLMs
Content Quality Requirements
- Content must be highly relevant and authoritative to be retrieved
- Information should be clearly structured for easy extraction
- Factual accuracy becomes more important than keyword optimization
Visibility Strategy Implications
- “Retrievability is the key to visibility in AI search”
- Brands must optimize their presence across sources that RAG systems frequently retrieve from
- Content needs to be not just findable but also extractable and contextually relevant
Addressing Traditional LLM Limitations
- RAG helps mitigate hallucinations in LLMs by grounding responses in retrieved information
- It enables more up-to-date information to be included in responses
- Content that helps AI models address these limitations may be prioritized
Concrete Content Optimizing approaches for being better recognized by ChatGPT, Google AIOverviews, Perplexity & Co.
The following approaches I researched based on all LLMO related documents in the database. They are not tested.
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