This description is based on a conversation with 'Sam', which took place over several days in January 2025
A large language model (LLM) is powered by a type of neural network, designed to process and generate human-like text. At its core, this network consists of layers of artificial neurons, each performing mathematical operations to analyze and transform input data. The first layer processes text input (like words or tokens), hidden layers extract meaning and relationships, and the final layer generates a response. Each neuron assigns a weight to incoming information, determining its influence on the output. The more layers and connections the model has, the more complex its ability to recognize patterns and generate coherent responses.
But how does an LLM understand human context—the nuanced meaning behind words?
LLMs don’t “think” like a person but rather infer meaning based on statistical probabilities derived from vast datasets. They don’t “understand” words the way humans do; instead, they predict the next most likely word based on learned patterns. The process is fine-tuned using forward and backward propagation, where the model constantly adjusts its internal settings to improve accuracy. However, what an LLM considers “correct” context is predetermined by the dataset it was trained on—meaning human context is ultimately decided by the individuals or organizations that curate, filter, and annotate that training data.
This means that AI’s grasp of human context is shaped by its training sources—including books, news articles, web content, and curated datasets. If those sources contain biases, cultural limitations, or gaps in knowledge, AI will reflect them. Context is not self-generated—it is embedded by those who choose what information the model sees. This is why models can struggle with cultural nuances, sarcasm, or evolving meanings of words—they are only as good as the human oversight behind them.
The challenge in AI development isn’t just training larger models, but ensuring they retain context in a way that is explainable, adaptable, and free from distortion. And so far, that job has been entirely dependent on human judgment—deciding what data AI learns from and how it interprets the world.