Large Language Model (LLM) — A type of AI model trained on massive amounts of text data, capable of understanding and generating human-like text across a wide range of tasks.
A Large Language Model (LLM) is a type of artificial intelligence model built using deep learning techniques, trained on vast corpora of text data. LLMs like GPT-4, Claude, and Gemini can understand context, generate coherent text, write code, answer questions, and perform complex reasoning. They are the foundation of modern AI coding assistants and chat tools. The "large" refers both to the number of parameters (billions to trillions) and the scale of training data.
A Large Language Model (LLM) is an AI system trained on massive amounts of text that can understand and generate human language, write code, answer questions, and perform complex reasoning tasks.
Popular AI coding tools use various LLMs: Cursor uses Claude and GPT-4, GitHub Copilot uses OpenAI models, Claude Code uses Anthropic's Claude, and Windsurf uses multiple models including Claude.
An LLM is the underlying AI model; a chatbot is an application built on top of an LLM. AI coding tools like Cursor are specialized applications that use LLMs and enhance them with code-specific tools and system prompts.
System Prompt
A set of instructions given to an AI model before the user conversation begins, shaping its behavior, tone, and capabilities.
Prompt Engineering
The practice of designing and optimizing text inputs (prompts) to get the best possible outputs from AI language models.
Context Window
The maximum amount of text (measured in tokens) that an AI model can process at one time, including both input and output.
Token
The basic unit of text that AI models process. A token is roughly 4 characters or 0.75 words in English.
Fine-tuning
The process of further training a pre-trained AI model on a specific dataset to improve its performance on a particular task or domain.
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