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.
Agents
Software that uses an LLM plus tools to plan and execute tasks toward a goal.
AI Alignment
The field of ensuring AI systems do what humans actually want them to do.
AI Analytics
Measuring how an AI system performs so you can improve quality, cost, and safety.
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 Engine
The core service or model that powers AI features behind the scenes.
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 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 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 Monitoring
Watching model quality, cost, and safety in production so you can react fast.
AI Optimization
Tuning prompts, models, and settings to improve quality, speed, and cost.
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 Prediction
Using a model to forecast an outcome or score before it happens.
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 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 Temperature
A setting that controls how predictable or creative an AI model’s outputs are.
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.
AI Wrapper
An application that adds a user interface on top of an existing AI model.
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.
Artificial General Intelligence (AGI)
A hypothetical AI that can learn and solve any intellectual task a human can.
Augmentation
The process of using AI to enhance human capabilities and productivity rather than replacing them.
Automation
A set of steps that run on their own so work moves forward without manual clicks.
Chain of Thought (CoT)
A prompting technique where the AI explains its reasoning step-by-step before giving an answer.
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 Caching
Saving the 'memory' of a long conversation or document so you don't have to re-process it every time.
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.
Cost Structure
The transformation of business expenses from variable labor costs to fixed, scalable software costs.
CRM
Software that tracks contacts, deals, and customer history in one place.
Data Source
The place your AI or app pulls truth from: a database, CRM, wiki, or file store.
Deep Learning
A subset of machine learning that uses multi-layered neural networks to learn from vast amounts of data.
Deepfake
Synthetic media where a person's likeness is convincingly swapped or generated by AI.
Few-Shot Prompting
Giving the AI a few examples of what you want before asking it to do the task.
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.
Generative AI
Models that create new text, images, code, audio, or video from patterns they learned.
GEO
Generative Engine Optimization: tuning content so generative search and chat pick it up accurately.
GPU Cluster
Thousands of graphics cards linked together to act as one supercomputer for training AI.
Grounding
Anchoring an AI's responses in real-world facts or specific data to prevent fabrication.
Labor Substitution
The direct replacement of human tasks and roles with automated AI systems.
Latency (Time to First Token)
The delay between sending a request and seeing the first word of the response.
LLM
A Large Language Model: a neural network trained to predict the next token and generate text.
LLM Marketing
Using language models to plan, create, and personalize marketing at scale.
Loss Function
The mathematical 'scorecard' an AI uses during training to measure how wrong its guess was.
LPU (Language Processing Unit)
Thinking chips designed specifically for running LLMs at extreme speeds.
Machine Learning
Using data and math to let software learn patterns instead of hardcoding rules.
Mixture of Experts (MoE)
A architecture that picks the best specialized 'expert' models for each part of a query.
Multimodal AI
AI that can understand and generate multiple types of media, like text, images, and audio.
Narrow AI (Weak AI)
AI designed to perform a specific task very well, but unable to do anything else.
Natural Language Processing
Techniques that let software understand, generate, and work with human language.
Neural Network
A computer system designed to mimic the way the human brain learns and processes information.
Parameters
The internal variables or 'settings' learned by the model during training.
Prompt
The input instructions and context you give an LLM.
Prompt Engineering
The skill of crafting inputs to get the best possible output from an AI model.
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.
System Prompt
The hidden 'master instruction' given to an AI that defines its persona and rules.
Tokens
The chunks of text an LLM reads and generates; cost and limits are usually token-based.
Transformer
The groundbreaking architecture introduced by Google in 2017 that made modern Generative AI possible.
Turing Test
A test to determine if a machine can exhibit behavior indistinguishable from a human.