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The advent of artificial intelligence-based search engines (ChatGPT, Perplexity, Gemini, Copilot, etc.) is radically transforming the way users search for and access information.
If traditional SEO aimed at positioning in Google results, today a new field is emerging: Generative Engine Optimization (GEO).
But the GEO ecosystem does not exist in isolation. A fundamental element of it is Retrieval-Augmented Optimization (RAO), which ensures that content is not only visible to generative engines, but also easily retrievable by artificial intelligence systems that use Retrieval-Augmented Generation (RAG) techniques.
🌐 What is Generative Engine Optimization (GEO)?
GEO is the art and science of making content citable and visible in AI-generated responses.
It's no longer just about appearing on the first page of Google, but about ensuring that a generative model chooses your content as a reliable source to summarize and report.
- Structure content clearly (titles, paragraphs, semantic markup).
- Improve authority and trust (E-E-A-T: experience, expertise, authority, reliability).
- Ensure content is up-to-date and fresh.
- Promote presence on platforms that AIs often use as sources (Wikipedia, forums, highly reputable sites).
Objective: Be recognized as a reliable and visible source by public generative AIs.
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| Example in Perplexity.ai |
🧩 Where does Retrieval-Augmented Optimization (RAO) come into play?
RAO is a more technical concept, involving the optimization of content for Retrieval-Augmented Generation (RAG) systems.
AI models don't know everything and often need to retrieve information from external databases (company documents, knowledge bases, websites). RAO serves to prepare content so that it can be retrieved and understood correctly by internal or semantic search engines.
- Create well-segmented text (chunking) to facilitate retrieval.
- Use metadata and schema markup to enrich documents.
- Adopt clear, contextualized language, which improves embeddings.
- Maintain semantic consistency to facilitate the matching between queries and content.
Objective: Be selected during the information retrieval phase by RAG-based systems.
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🔗 GEO and RAO: parts of the same ecosystem
Even though GEO and RAO have different purposes and contexts, they are part of a single AI optimization ecosystem.
- RAO is upstream → it ensures that content is retrieval-friendly and therefore accessible to AI systems.
- GEO is downstream → it ensures that content, once retrieved, is also recognized, summarized, and cited in responses.
In other words: without a good RAO, content risks not being retrieved. Without a good GEO, even if retrieved, it risks not appearing in the generated response.
🛠️ Practical Strategies for Operating in the GEO Ecosystem
- Optimize for Citability (GEO): Write clear, authoritative, and up-to-date content.
- Optimize for Retrieval (RAO): Segment texts, add metadata, use structured markup.
- Think Multichannel: Content must be accessible to both public generative engines (GEO) and specific AI systems such as enterprise chatbots (RAO).
- Monitor AI Visibility: Use emerging tools (e.g., Wix's AI Visibility Overview) to understand how and where content is being picked up by LLMs.
🚀 Conclusion
The concept of the GEO ecosystem does not replace SEO, but complements and expands it. In this New scenario:
- GEO ensures the presence of brands in the responses of generative AI.
- RAO makes content technically suitable for retrieval by RAG systems.
Those who master both sides—marketing and technical—will have a decisive competitive advantage in a world where users no longer click, but ask artificial intelligence directly.
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