Why this matters
Large language models (LLMs) power the chat assistants many people now use for writing, summarising and answering questions. Ghanaian employers advertise for engineers who can build applications on them, fine tune them, and design prompts. To do that work well, and safely, you need to understand what these models actually do, because it is not what most people assume.
What an LLM does
An LLM is a very large neural network trained to do one thing: predict the next token of text. Given "The capital of the Northern Region is", it assigns a probability to every possible next token and picks one. Then it adds that token to the text and predicts again, one piece at a time, until it stops.
Trained on enormous amounts of text, this simple task produces models that can answer questions, summarise, translate and write code. Most modern LLMs use a design called the transformer, whose key idea, attention, lets each token look at every other token in the text to decide what matters for the next prediction.
After this first training, models are usually tuned further on examples of helpful answers and human feedback, which makes them better at following instructions.
Why LLMs state false things confidently
An LLM predicts text that sounds likely. It has no built in way to check facts. When it does not know something, it can still produce fluent, confident text that is wrong. This is often called . It may invent a statistic, a law section, a reference or a person. Everything an LLM produces for real use must be checked, especially anything about health, money or the law.