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.
AEO
Answer Engine Optimization: helping AI chat and search surfaces return clear, source-backed answers.
AI Automation
Automations powered by models that can read, decide, and act with your data.
AI Chat Interface
A chat-shaped surface that lets users talk to an AI inside your product.
AI Compliance
Meeting legal and policy requirements when you build and run AI features.
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 Governance
Policies, processes, and oversight that guide how AI is built and used.
AI Hallucination
When an AI confidently makes up facts or details that are not grounded in real data.
AI Integration
Connecting an AI model to your product or process so it can act on real data.
AI Knowledge Base
A living library of answers and sources that an AI can search to respond accurately.
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 Platform
A set of tools and services that let you build, ship, and manage AI features in one place.
AI Recommendation
Models that suggest the next best item, action, or piece of content.
AI Search
Search that understands meaning, not just keywords, to surface the best answer fast.
AI Security
Protecting models, data, and outputs from abuse, leaks, and tampering.
AI System
All the pieces that make an AI feature work: models, data, tools, and guardrails together.
AI Training
Teaching a model with data and feedback so it learns patterns that generalize.
Algorithmic Bias
When AI creates unfair outcomes because of prejudices in its training data.
API
A set of rules that let one piece of software talk to another.
Augmentation
The process of using AI to enhance human capabilities and productivity rather than replacing them.
Fine-tuning
The process of training a pre-existing AI model on specific data to make it an expert.
Foundation Model
A large-scale AI model trained on vast data that serves as a base for many applications.
Function Calling
The ability of an AI to connect to external tools and execute code rather than just talking.
Labor Substitution
The direct replacement of human tasks and roles with automated AI systems.
LLM
A Large Language Model: a neural network trained to predict the next token and generate text.
LPU (Language Processing Unit)
Thinking chips designed specifically for running LLMs at extreme speeds.
Sentiment Analysis
AI that determines the emotional tone behind a piece of text.
SEO
Search Engine Optimization: helping web content rank and get discovered in traditional search.
SLM (Small Language Model)
A compact AI model designed for efficiency and specific tasks rather than general knowledge.
Supervised Learning
Training AI using labeled examples, like a teacher with an answer key.
Synthetic Data
Fake data created by AI to train other AI models.