Understand AI terms in plain English
An AI system that can autonomously perform tasks, make decisions, and interact with other systems to achieve goals. Unlike simple chatbots, agents can take actions.
A way for software applications to communicate with each other. AI APIs let developers integrate AI capabilities into their apps.
An AI program designed to simulate conversation with human users, typically through text. Examples include ChatGPT, Claude, and customer service bots.
The amount of text an AI can "remember" in a conversation. Larger context windows (like Claude's 200K tokens) mean the AI can work with longer documents.
AI systems that can create new content — text, images, audio, video, or code — rather than just analyzing existing data.
The architecture behind ChatGPT. A type of large language model trained to predict and generate human-like text.
AI systems trained on vast amounts of text data to understand and generate human language. Examples include GPT-4, Claude, and Gemini.
A subset of AI where computers learn patterns from data without being explicitly programmed for every scenario.
AI systems that can understand and work with multiple types of input — text, images, audio, and video — simultaneously.
Computing systems inspired by biological brains, composed of interconnected nodes (neurons) that process information in layers.
The input or instruction you give to an AI to get a response. Better prompts lead to better outputs.
The art of crafting effective prompts to get better results from AI. Includes techniques like chain-of-thought, few-shot learning, and role prompting.
A technique where AI retrieves information from a knowledge base before generating responses, making answers more accurate and current.
Units of text that AI models process. Roughly 1 token = 4 characters or 0.75 words. Context windows and pricing are often measured in tokens.
The neural network architecture behind modern LLMs. Enables AI to understand relationships between words across long distances in text.