LLM

A Large Language Model (LLM) is an artificial intelligence system trained on enormous collections of text — books, websites, articles, and more. By studying patterns in this text, the model learns to predict what word or phrase should come next. This ability lets it answer questions, write essays, summarise documents, translate languages, and hold conversations.\n\nLLMs do not truly "understand" language the way humans do. Instead, they are extraordinarily sophisticated pattern-matching engines that produce text that often reads as if it were written by a knowledgeable person.

LLMs power chatbots like ChatGPT, coding assistants, search engines, content generation tools, and document analysis systems. Developers integrate them via APIs to add natural language capabilities to applications.

When you ask ChatGPT to explain quantum computing in simple terms, an LLM processes your question and generates a response by drawing on patterns learned during training. The same technology helps GitHub Copilot suggest code as you type.

LLMs do not have consciousness or genuine understanding. They can produce confident-sounding but incorrect information ("hallucinations"). They also reflect biases present in their training data.

The transformer architecture (2017) enabled modern LLMs. GPT-2 (2019) demonstrated surprising text generation. GPT-3 (2020) showed few-shot learning at scale. ChatGPT (2022) brought LLMs to mainstream awareness.