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Most people interested in generative AI likely already know that Large Language Models (LLMs) — like those behind ChatGPT, Anthropic’s Claude, and Google’s Gemini — are trained on massive datasets: trillions of words pulled from websites, books, codebases, and, increasingly, other media such as images, audio, and video. But why?
From this data, LLMs develop a statistical, generalized understanding of language, its patterns, and the world — encoded in the form of billions of parameters, or “settings,” in a network of artificial neurons (which are mathematical functions that transform input data into output signals).
By being exposed to all this training data, LLMs learn to detect and generalize patterns that are reflected in the parameters of their neurons. For instance, the word “apple” often appears near terms related to food, fruit, or trees, and sometimes computers. The model picks up that apples can be red, green, or yellow, or even sometimes other colors if rotten or rare, are spelled “a-p-p-l-e” in English, and are edible. This statistical knowledge influences how the model responds when a user enters a prompt — shaping the output it generates based on the associations it “learned” from the training data.
