What is Large Language Model?

Large language models are neural networks trained on hundreds of billions of tokens of text — web pages, books, code, academic papers — to learn the statistical patterns of language. This training enables them to predict likely next words with enough sophistication to generate coherent, contextually appropriate responses to virtually any text input. The "large" in LLM refers to the scale of both training data and model parameters (the internal weights that encode learned patterns), which now number in the hundreds of billions.

LLMs work through a mechanism called the Transformer architecture, which enables the model to weigh the relevance of all parts of an input when generating each word of the output. This "attention" mechanism is what allows LLMs to maintain coherence over long passages, follow complex multi-step instructions, and adapt their tone and style to context. The models are initially trained on general text, then fine-tuned with human feedback to follow instructions reliably and avoid harmful outputs.

The leading LLMs include OpenAI's GPT-4 and o-series models (powering ChatGPT), Anthropic's Claude series, Google's Gemini, and Meta's open-source Llama family. Each model family has different strengths — some excel at coding, others at reasoning, others at following nuanced instructions. Businesses use LLMs directly through chat interfaces or via API to build LLM-powered features into their own products.