Large Language Model (LLM)
A type of AI model trained on vast amounts of text to predict the next word in a sequence. Powers ChatGPT, Claude, Gemini, and similar tools.
A large language model (LLM) is a neural network trained on enormous amounts of text data — typically hundreds of billions of words — to predict the next word in a sequence. Despite this seemingly simple objective, LLMs develop emergent capabilities including reasoning, translation, summarisation, and code generation.
Modern LLMs use the transformer architecture introduced in 2017, which allows the model to weigh which parts of the input matter most when producing each output token. Famous examples include OpenAI's GPT series (powering ChatGPT), Anthropic's Claude, Google's Gemini, and open-source models like Meta's Llama.
LLMs are typically trained in two stages: pre-training on broad text corpora to learn language patterns, then fine-tuning with techniques like RLHF (reinforcement learning from human feedback) to align outputs with human preferences. Limitations include hallucinations (producing plausible but false statements), lack of persistent memory, and inability to update knowledge without retraining.