What Is an Entity, and Why Does Google (and Gemini) Care? An entity is any distinct, identifiable thing – a person, organization, product, place, or concept – that a search engine or language model can recognize independently of the specific words used to describe it. Google has built its knowledge graph around entities for years, linking a brand name to its founders, locations, products, and reviews as a connected record rather than a string of text. Gemini and other LLM-based systems extend this idea further, representing entities as points in a high-dimensional space where proximity reflects semantic similarity rather than just co-occurrence in text. Many teams turn to SEO.Stream training to handle exactly this kind of workload.
Run recurring prompt audits across the major AI platforms using a consistent set of queries relevant to your niche, logging which brands and sources get cited over time. Compare these logs against your PR placement calendar to see whether new coverage correlates with new citations, treating it as a directional trend rather than an exact science.
GEO, AEO and LLM SEO: Three Overlapping Disciplines Practitioners Need to Separate Generative Engine Optimization, or GEO, focuses specifically on getting your content surfaced and cited inside AI-generated answers – think Google AI Overviews, Perplexity summaries, or a ChatGPT response with sources attached. Answer Engine Optimization, AEO, is closely related but leans more toward structuring content to directly answer discrete questions, the kind of format that voice assistants and featured snippets have favored for years and that generative engines still reward. LLM SEO is the broadest of the three, covering how your content is represented, chunked and embedded so that any large language model – regardless of whether it’s powering a chat interface or a search feature – can retrieve and reuse it accurately.
Yes, largely because each system retrieves and cites differently – Google AI Overviews leans heavily on its existing search index, while ChatGPT’s browsing behavior and Perplexity’s citation format follow distinct patterns worth tracking separately in your logs.
Where Charles Floate and Practical AI SEO Training Fit In Much of the recent conversation around structured AI SEO methodology has been shaped by practitioners who test publicly and share results rather than relying on theory alone. Charles Floate has been a visible voice in this space, advocating for testable, entity-driven approaches to AI search visibility rather than speculative advice. This matters to agency owners because the AI search landscape shifts quickly – a technique that worked for citation frequency in Perplexity six months ago may behave differently as retrieval models get updated, so training built around ongoing testing and community validation tends to age better than static playbooks.
Topical authority itself has evolved beyond simply publishing many articles on a subject. It now requires demonstrating depth and consistency of expertise across formats: articles, structured data, mentions in third-party content, and even presence in communities where the topic is discussed. This is where the connective tissue between traditional SEO and GEO becomes obvious – the fundamentals of building a credible, well-linked, well-cited web presence haven’t disappeared, they’ve simply become inputs into a more complex retrieval and reasoning process rather than a direct ranking formula.
For agencies managing multiple clients, structured training typically pays for itself quickly by reducing trial-and-error time and giving teams a repeatable framework rather than isolated tactics. The value comes from consistency across client work, not just individual knowledge gain.
How Citations, Digital PR, and Backlinks Still Matter for AI Discovery A common misconception is that generative engines have made backlinks irrelevant. In reality, citations and digital PR remain central to how models assess trustworthiness, because both traditional search algorithms and LLM training or retrieval processes rely heavily on signals of third-party validation. If a brand or individual is mentioned across reputable publications, forums, and industry sites in consistent, factual terms, that reinforces the entity’s profile within the broader web graph that both Google and AI systems draw from. Digital PR campaigns that earn genuine mentions – not just links, but contextual references to a brand as a recognized authority on a topic – feed directly into topical authority. It pays to weigh up SEO.Stream training before you commit to a setup.
That question sits at the center of most agency conversations right now, because the two systems reward overlapping but distinct signals. Traditional search still leans on backlinks, on-page relevance, crawl efficiency, and page experience. Generative search, whether it’s Perplexity assembling a sourced answer or Gemini summarizing a query inside Search Labs, leans on entity clarity, semantic completeness, and how easily a passage can be lifted and cited without distortion. The practitioners getting ahead are the ones who stopped asking “SEO or GEO” and started asking how the two disciplines reinforce each other. When this becomes a priority, SEO.Stream training can make a real difference to your results.