\#Training Data — Blog — BrandGEO A Markdown version of this page is available at https://brandgeo.co/blog/tag/training-data.md, optimized for AI and LLM tools. \#Training Data ================= 4 posts in #Training Data Jun 6, 2026 [AI Visibility](https://brandgeo.co/blog/category/ai-visibility) [The Visibility Gap: Why Your Brand Scores Differently in AI's Memory vs Live Search](https://brandgeo.co/blog/the-visibility-gap-ai-memory-vs-live-search) ------------------------------------------------------------------------------------------------------------------------------------------------------------- Ask ChatGPT about your brand with web browsing off and you get one answer — drawn from training data, the reputation baked into the model. Turn browsing on and you can get a different answer entirely, assembled from whatever the model finds on the live web in that moment. Most measurement programs only ever see one of these. The gap between them is diagnostic: it tells you whether the live web is rescuing a weak memory, or quietly eroding a strong one. This post is about why the gap exists, what its sign and size mean, and how to act on each case. Apr 8, 2026 [AI Visibility](https://brandgeo.co/blog/category/ai-visibility) [Anatomy of an LLM Answer: Where Your Brand Fits In the Recipe](https://brandgeo.co/blog/anatomy-of-an-llm-answer-where-your-brand-fits) ------------------------------------------------------------------------------------------------------------------------------------------ A large language model does not keep a database of brands. It does not look up your company the way a search engine queries an index. When someone asks ChatGPT or Claude about your category, the model assembles an answer from several overlapping sources — parametric memory, any available retrieval, and the running context of the conversation. Understanding how that assembly works is the difference between guessing at GEO tactics and choosing them deliberately. This post walks through the recipe. Apr 1, 2026 [AI Visibility](https://brandgeo.co/blog/category/ai-visibility) [Training Data vs. Real-Time Retrieval: The Two Ways LLMs Know Your Brand](https://brandgeo.co/blog/training-data-vs-real-time-retrieval-llm-brand-knowledge) --------------------------------------------------------------------------------------------------------------------------------------------------------------- Ask ChatGPT about your brand twice — once with browsing enabled, once without — and you often get two different answers. That is not a bug. It is the visible surface of a deeper structure: language models hold brand knowledge in two distinct places, training data and real-time retrieval, with very different properties. Treating them as the same thing is how marketing teams end up applying the wrong fix to the wrong gap. This post walks through both paths and the tactical implications of each. Mar 2, 2026 [AI Visibility](https://brandgeo.co/blog/category/ai-visibility) [Brand in the Model's Memory vs. Brand in the Model's Context](https://brandgeo.co/blog/brand-in-models-memory-vs-context) ---------------------------------------------------------------------------------------------------------------------------- A subtle distinction shapes almost every practical decision in AI brand visibility. There is the brand as the model has learned it — baked into its parameters from training data. And there is the brand as the model describes it in a specific answer, shaped by retrieval, the user's question, the conversation history, and post-processing. The first is memory. The second is context. Conflating the two is how teams end up fixing the wrong thing. The distinction is simple once you name it, and useful once you use it. 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