Learn the language of automation.
A simple guide to the terms we use every day. Clear explanations to help you understand how the technology works.
Agents
Software that uses an LLM plus tools to plan and execute tasks toward a goal.
AI Analytics
Measuring how an AI system performs so you can improve quality, cost, and safety.
AI Decision Making
Using models to choose or recommend actions based on data and goals.
AI Deployment
The process of serving a trained model so real users and systems can call it.
AI Engine
The core service or model that powers AI features behind the scenes.
AI Inference
Running a trained model to get an output for a real request.
AI Integration
Connecting an AI model to your product or process so it can act on real data.
AI Model
The learned function that maps inputs to predictions, decisions, or generated content.
AI Personalization
Using AI to tailor content and actions to each person’s context and history.
AI Recommendation
Models that suggest the next best item, action, or piece of content.
AI Software
Software that bakes AI into the product so users get smarter help by default.
AI System
All the pieces that make an AI feature work: models, data, tools, and guardrails together.
AI Tool
A focused app or feature that uses AI to solve one job well.
AI Training
Teaching a model with data and feedback so it learns patterns that generalize.
API
A set of rules that let one piece of software talk to another.
Automation
A set of steps that run on their own so work moves forward without manual clicks.
Chatbot
A conversational interface that lets people talk to software in plain language.
Computer Vision
Teaching software to interpret images and video so it can detect, classify, and describe what it sees.
Context window
The maximum amount of tokens an LLM can consider at once (input + output).
Copilot
An AI assistant that works alongside you to suggest improvements or automate tasks.