Large Language Models (LLMs) are AI systems trained to understand and generate human-like text — they’re the brains behind tools like ChatGPT, Google Bard, and Claude.
The term “large” refers to their scale: LLMs are trained on massive amounts of text data from books, websites, and conversations, and contain billions (or even trillions) of parameters. These parameters are like memory knobs that help the model capture grammar, facts, tone, logic, and nuance.
LLMs don’t think or understand like humans — they predict what words are likely to come next, based on patterns they’ve seen. But the results can feel surprisingly smart: they can write essays, summarize news, answer questions, translate languages, and even write code.
They’re built using architectures like transformers, which allow the model to process context more effectively. And while powerful, they also raise concerns: bias, hallucination (inventing facts), and ethical use.
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