How does an AI system actually know that "cheap running shoes" and "affordable trainers" mean roughly the same thing? Why does Google's AI Overviews sometimes cite a smaller site over a well-known publisher, and how does Perplexity decide which paragraph deserves a citation versus which gets ignored entirely? These questions sit at the center of a technical concept called embeddings, and understanding them has quietly become one of the most valuable skills a modern SEO professional can develop.
Manual spot-checking target queries in an incognito browser remains the most reliable method today, supplemented by rank-tracking tools that have added AI Overview detection features. Standard analytics platforms don't yet isolate this traffic cleanly, so combining manual checks with tool-based tracking gives the most accurate picture.
Yes, because embeddings reward semantic relevance and information gain rather than domain size or budget alone. A narrowly focused, well-documented piece of content from a small site can outscore a broad, generic page from a larger competitor if it answers the specific query more precisely.
How Does Retrieval Actually Work Inside Tools Like Gemini and Perplexity? Retrieval is the step where a system searches its index of embedded content to find passages most relevant to a user's query, before any generative answer gets written. Picture a librarian who has already organized every book not alphabetically but by meaning, so a request about "reducing customer churn" pulls neighboring shelves labeled "retention strategy," "subscription cancellation," and "loyalty programs," even though none of those exact words appeared in the request. That is retrieval-augmented generation in a nutshell, and it's the process running quietly behind Google AI Overviews, Gemini, and Perplexity whenever they assemble an answer.
Structured courses tend to compress the learning curve by providing tested frameworks and cohort feedback, which is harder to replicate from scattered articles alone, though combining both approaches generally works best for practitioners with some existing SEO background.
No, and doing so would likely hurt both efforts. Backlinks, technical health and on-page relevance still influence whether a page enters the retrieval pool that AI systems draw citations from, so traditional SEO remains the foundation GEO training program builds on top of.
Why Google AI Overviews Behave Differently From Classic Search Rankings Traditional SEO trained an entire industry to optimize for a ranked list: ten blue links, judged largely by backlinks, on-page relevance and user engagement signals. Google AI Overviews instead generate a synthesized paragraph by retrieving passages from multiple sources, weighing them against a model's internal understanding of the topic, and stitching together an answer that may cite three or four sources out of dozens that could have qualified. This means visibility now depends on passage-level clarity as much as page-level authority, because the retrieval layer is scanning for self-contained chunks of text that directly answer a specific sub-question rather than scanning an entire article for general topical relevance.
This is why information gain matters so heavily in AI search visibility. If ten competing pages all say the same generic thing about churn reduction, their embeddings cluster together and none stands out enough to be prioritized. A page that adds a distinct, well-supported angle, a genuinely new data point, or a clearer framework creates separation in that vector space, giving retrieval systems a stronger reason to select it. Agencies that study this dynamic through structured training like AI SEO Rainmakers tend to build content audits specifically designed to identify where a page is semantically redundant versus where it offers real incremental value.
Run the query manually across Gemini, Perplexity, and Google AI Overviews to see whether your page, or a close paraphrase of it, gets surfaced or cited, and note which competitor content appears instead.
Presence and framing changes can sometimes show up within two to four weeks of sampling, but citation quality and authority-driven shifts often take two to three months, since they depend on backlink accrual and knowledge graph updates that don't happen instantly.
From Keywords To Entities: What GEO And AEO Actually Optimize For Generative Engine Optimization and Answer Engine Optimization both shift the unit of optimization from keywords to entities and relationships. An entity is any distinct, identifiable thing - a brand, a person, a product category, a concept - that a knowledge graph can link to other entities through defined relationships. When Gemini, Perplexity or ChatGPT answer a query, they are not simply matching strings; they are reasoning across an internal representation of entities and the semantic distance between them, often reinforced by embeddings that place conceptually similar text close together in vector space regardless of exact wording.