Training Data

Modern LLM corpora combine large-scale web crawls, gathered by crawlers like GPTBot and ClaudeBot or via datasets like Common Crawl, with books, academic text, code, and licensed publisher content. Data is filtered for quality and deduplicated, meaning authoritative, well-maintained pages are far likelier to survive into training than thin or spammy ones.

Each lab's mix differs, which is one reason ChatGPT, Claude, and Gemini describe the same brand differently: they literally learned from different snapshots of the web.

During training the model compresses its corpus into statistical associations: which brands belong to which categories, what they are praised or criticized for, who their competitors are. When a user asks for recommendations without web search, the answer is a readout of those associations, your reputation as the web stated it months or years ago, frozen until the next knowledge cutoff.

Frequency and authority both matter: a brand described consistently across many reputable sources develops a strong, accurate representation; one mentioned rarely gets a weak, hallucination-prone one.

You cannot edit a trained model, but every future model trains on the web you are building now. Keep AI crawlers allowed in robots.txt, maintain consistent brand facts everywhere, and invest in durable third-party coverage, reviews, comparisons, press, documentation, since models weight independent sources heavily. This is the slow compounding layer of GEO strategy, complementing fast retrieval-side wins.

Watching answers change after major model releases reveals whether your footprint is improving; tracking the same prompts before and after a release makes the training-data effect directly observable.