LLMs are the engines behind modern language assistants, chatbots and text-generation tools. They operate by predicting the next token in a sequence, but with billions of parameters and huge training corpora they can do much more - write code, summarize documents, generate creative text and even reason about complex problems.
Under the hood, LLMs use the transformer architecture, a mathematical structure that enables models to capture context, tone and semantics across long spans of text. That’s why they don’t just "read" words but can model how they relate to one another. Prominent examples include GPT, Claude, Gemini and LLaMA, each with different capabilities and design trade-offs.
LLMs enable new ways of interacting with technology. Instead of navigating menus or writing line-by-line code, you can communicate in natural language. As models continue to improve, interactions become more natural and useful - like having a digital collaborator (without coffee breaks).
