Abstract
A grandmother pauses before answering a child. The meaning of her words lies not only in the words themselves, but in tone, shared history, the relationship between speaker and listener, the setting, and even the silence that precedes the response. None of these become part of the training data for today’s large language models (LLMs). As Artificial Intelligence (AI) expands into African languages, discussions often celebrate new datasets, multilingual benchmarks, and translation systems. Yet something more fundamental is at stake. AI does not simply automate language. It transforms what counts as language into a textually legible dataset and abstracts language from the context of its production. A Large Language Model (LLM) is an AI system that has learned patterns from large amounts of words. This allows it to understand what people write or say and generate human-like responses, making it useful for tasks such as answering questions, drafting documents, translating languages, and assisting with problem-solving.