At its core, a large language model (LLM) is like an incredibly sophisticated predictive system trained on massive datasets (billions of pages of text) to learn patterns and relationships in language. It has read millions of books, articles, and web pages.
When you give it a prompt, the model analyzes the text and predicts what word or "token" should come next, then continues this process word by word to generate a complete response. Think of it like having a conversation partner who has absorbed vast amounts of human writing and can anticipate where a sentence is heading based on patterns it has learned. The model doesn't truly "understand" in the way humans do, but it has become remarkably good at recognizing patterns in language and producing text that feels natural and coherent.
Like all machine learning models, a large language model requires training and fine-tuning before it’s ready to output the expected and needed results. Training datasets consist of trillions of words, and their quality is set to affect the language model's performance. At this stage, the large language model engages in unsupervised learning, meaning it processes the datasets fed to it without specific instructions.
During this process, the algorithm learns to recognize the statistical relationships between words and their context. For example, it would learn to understand whether "right" means "correct," or the opposite of "left."
