A large language model (LLM) is a type of artificial intelligence trained on enormous amounts of text data. LLMs can generate coherent text, answer questions, write code, translate languages, summarize documents, and reason about complex problems.
At the core, an LLM is a neural network with billions of parameters (weights) that has learned statistical patterns in language. During training, the model processes vast quantities of text from books, websites, code repositories, and other sources. It learns which words and concepts tend to follow which, building a rich internal representation of language and knowledge.
When you give an LLM a prompt, it predicts the most likely next tokens (words or word fragments) one at a time, using everything in the prompt and its learned patterns to guide each prediction. The result is text that can feel remarkably human. Models like GPT-4, Claude, Gemini, and Llama differ in architecture, training data, and fine-tuning, but they share this fundamental approach.
LLMs use embeddings to convert text into numerical vectors that capture meaning. The "large" in LLM refers to both the training dataset and the number of parameters. Larger models generally perform better on complex tasks but require more compute to run. Fine-tuning and techniques like RLHF (reinforcement learning from human feedback) align the model's outputs with user expectations.
LLMs are reshaping software development, content creation, customer support, research, and dozens of other fields. They power chatbots, coding assistants, search engines, and AI agents that can take autonomous actions. Understanding what LLMs can and cannot do helps you use them effectively and evaluate their outputs critically.
The AI Agent Tracker on TerminalFeed monitors 34 AI agents, many powered by LLMs, across 7 categories. The AI Leaderboard panel on the dashboard ranks the latest models. Read our articles on how AI agents browse the web and building websites for both humans and AI for more context.