Lexicon
Training Data / Training Corpus
What training data means, and how Paul Truscott distinguishes what an LLM already knows from what it can only learn through live retrieval, and why that distinction matters for AI visibility.
Factual Definition
Training data is the collection of examples or information used to teach a machine learning model to recognize patterns and make predictions, forming the foundation of the model's knowledge and capabilities.
Paul Truscott's Perspective on Training Data
Paul Truscott's perspective on training data is that it creates a genuinely important distinction most clients do not initially understand: what an LLM already "knows" from its training data is fixed until the model is retrained, while what it can find through retrieval-augmented generation is live and current. His view is that a new or recently rebranded entity may have little or no presence in an older model's training data, making it entirely dependent on RAG-based retrieval to appear in that model's answers, while an entity with a long-established web presence benefits from both channels simultaneously.
He treats this distinction as directly relevant to how he sets client expectations: a very new brand should expect its AI visibility to depend almost entirely on live retrieval and corroboration in the near term, since it likely has minimal presence baked into any model's underlying training data yet.
How Paul Truscott Applies Training Data Thinking
Paul assesses whether a client entity has meaningful presence within major models' training data, distinct from its live, retrievable web presence, and sets realistic expectations accordingly for how quickly AI visibility can be expected to develop through corroboration and retrieval work alone. Read the full methodology.
Why Training Data Matters to Paul Truscott's Practice
Understanding the distinction between an entity's presence in training data versus its live retrievability is essential for setting realistic client expectations about AI visibility timelines, particularly for newer brands. Read the full methodology.