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Forget neat rows of facts—your brand lives inside AI as a point on a vast map of meaning. We unpack how large language models like ChatGPT convert words into vectors, arrange them in a multi‑dimensional latent space, and “reason” by navigating probabilistic paths rather than retrieving certified entries from a knowledge graph. That shift explains both the astonishing creativity of LLMs and the stubborn problem of hallucinations, and it reveals why your content choices directly influence how machines see you.

We start by separating Google’s Knowledge Graph—built on labelled, verifiable relationships—from the statistical engine that powers LLMs. From there, we walk through tokens, embeddings, and the geometry of meaning: why “king” sits near “queen,” how “bank” splits by context, and how directions in vector space encode relationships like gender or capital cities. Then we explore probabilistic reasoning and chain‑of‑thought prompting, showing how stepwise guidance can reduce errors by constraining the model’s path through its internal map.

The practical payoff is clear: you can shape your brand’s coordinates. Consistent naming, precise definitions, structured internal linking, authoritative citations, and schema markup help AIs place you in the right neighbourhood of concepts. Pillar pages and topical clusters reinforce the connections that matter, while concise fact sheets and retrieval‑ready content give models the anchors they need to avoid plausible-but-wrong continuations. Think of every page as another vector pull toward accuracy; over time, your credibility becomes the shortest path the model can take.

If this helped you see how AI really “thinks” about your brand, follow the show, share it with a colleague, and leave a quick review. Got a question you want answered on air? Send a voice message via the link in the show notes and tell us where you want your brand’s coordinates to land.

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"Werq" Kevin MacLeod (incompetech.com)
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