How to Build up your Generative AI Optimization Engines – Part 1

Generative engine optimization

Generative AI (generative engine) is any artificial intelligence (AI) that can generate content (text, images, audio, video, code, or a combination of many) as opposed to being merely a retriever or classifier of existing content and is deemed to be part of generative AI. Generative AI systems and models are composed of deep learning models, including neural networks, which make them a part of artificial intelligence. Optimization for Generative Engines is a subset of Serch Everywhere Optimization. This is a two part article, where I have separated the basics from the pratical  work.

Highlights

    • Generative AI & LLMs – Generative AI creates new content (text, images, video, audio, code) rather than simply retrieving information. Large Language Models (LLMs) like GPT-4 fall within this category, alongside models using GANs, RNNs, and hybrid neural networks.
    • Generative Engine Optimization (GEO) – A new branch of SEO focused on making content discoverable and quotable by generative AI systems. It emphasizes understanding user intent, providing EEAT-rich content, and optimizing for AI-driven search environments.
    • Two Core Strategies – Influence foundational models (often difficult for most creators) and optimize for Retrieval Augmented Generation (RAG), ensuring your content is chosen as a source and cited often.
    • 10 Practical Steps for GEO –
        1. Ensure your site is crawlable by LLM bots.
        2. Maintain strong traditional SEO rankings.
        3. Target “query fanout” keywords generated by LLMs.
        4. Keep brand mentions consistent across platforms.
        5. Avoid heavy reliance on JavaScript for core content.
        6. Engage on UGC-heavy platforms like Reddit and Wikipedia.
        7. Write in machine-readable, quotable formats with schema and clear facts.
        8. Optimize AI-generated content for unique terms, pros/cons, expert quotes, and structured data.
        9. Stick to verifiable facts and introduce original data.
        10. Invest in digital PR to increase brand authority and citations.
    • Workflow & Tactics – Use GEO tools to identify competitors, analyze high-authority sources, target keywords from query fanouts, and leverage PR, influencer marketing, and affiliate opportunities to secure citations in AI responses.

Genrative AI Vs. Large Language Models

Generative AI allows the creation of content on a diverse variety of outputs, including generated text, image generation (such as realistic images), video generation, music generation, and voice cloning. The use of AI-generated media and content is becoming widespread, enabling the production of content at scale through generative artificial intelligence.

The Generative Engines that only have natural language processing characteristics fall under the large language models (LLMs). These are also a form of foundation models and are instances of advanced models that employ machine learning and neural networks. The generative AI models also employ generative adversarial networks (GANs), recurrent neural networks, and architectures that combine two neural networks. Data augmentation techniques utilize generative AI models to train machine learning models, relying on extensive data sets that contain synthetic and structured data. You can address complex problems in various fields by leveraging outputs from numerous generative AI models that demonstrate high levels of advanced capabilities.

In this article, we will discuss Generative Engine Optimization (GEO), a sub-form of Search Everywhere Optimization (SEO). The initial process of any successful GEO campaign is to generate content that Large Language models will want to link to or cite. To create that content, you need to understand your users’ intent! What is it that the user is interested in finding out, and what is his reason to come seeking answers? Unlike traditional search engines, you are not just optimizing content on sites; you are developing a comprehensive understanding of who, what, where, when, why, and how this content relates to your product or service. The users are not even on your site, and you have to guess the condition that leads them to perform the search.

GEO Strategy Components

Consider experiences that you wouldn’t typically expect to find directly within ChatGPT or similar systems:

    • Engaging content like a 3D tour of the Louvre or a virtual reality concert. Generative AI can also automate the creation of web pages and digital assets, making it easier to deliver interactive and personalized experiences.
    • Live data includes prices, flight delays, and available hotel rooms. While LLMs can integrate this data via APIs, I see the opportunity to capture some of this traffic for the time being.
    • Topics that require EEAT (experience, expertise, authoritativeness, trustworthiness).

Users want a firsthand experience, but LLMs do not have one. Thus, the issue motivates LLMs to cite sources where the knowledge resides and can be accessed firsthand. Well, that is only one critical consideration; what then are the others?

We should differentiate between 2 strategies: the role of influencing the basis of the model and the role of grounding as an instructional tool. Whereas the former is mainly out of reach of most creators, the latter holds opportunities. To succeed, new SEO developments must incorporate improved AI tools to aid content creation and optimization, according to GEO.

Influencing Foundational and Large Language Models

The foundational models have a pre-determined set of data and are not able to learn anything outside their training sets once they are trained. These datasets can incorporate synthetic data, structured data, and data augmentation methods to enhance the performance and robustness of the models. On existing systems such as GPT-4, it is too late – such systems have already been trained.

However, this is relevant towards the future: a so-called refrigerator that is operating on o4-mini in 2025 and which, in theory, may have a preference towards Coke rather than Pepsi. This prejudice may affect purchasing decisions in the future.

RAG

Optimizing For Retrieval Augmented Generation (RAG)/Grounding

When large language models (LLMs) are unable to produce answers based solely on their training data, they employ the retrieval augmented generation (RAG) technique to incorporate new information and provide an answer. Such systems as AI Overviews or ChatGPT web search are based on this approach. RAG combines information retrieval and generative model outputs to provide contextually more precise and contextually appropriate answers, resulting in improved contextual knowledge of the system.

As SEO professionals, we want three things:

    1. Our content gets selected as a source.
    2. Our content is most frequently quoted within those sources.
    3. Other selected sources support our desired outcome.

Concrete Steps To Succeed With GEO

Don’t panic – there is no rocket science involved in optimizing your content and referencing your brands when using large language models. To the contrary, a lot of the old SEO strategies will still work, and only a couple of new ones will need to be implemented into your routine. AI assistants and AI agents can also be used to automate and simplify your GEO operations, allowing you to streamline content optimization and management processes more easily.