AI Glossary
Plain-English explanations of AI terms for business owners. No jargon, no PhD required.
AI Agent
Software that can understand requests, make decisions, and take actions autonomously. Unlike simple chatbots, AI agents can perform tasks like booking appointments, sending emails, or updating records without human intervention.
Example
An AI agent that handles customer service can understand a complaint, look up order history, process a refund, and send a confirmation email, all automatically.
Artificial Intelligence (AI)
Computer systems designed to perform tasks that typically require human intelligence, such as understanding language, recognizing patterns, making decisions, and learning from experience.
Example
AI powers voice assistants, recommendation engines, fraud detection, and customer service automation.
Automation
Using technology to perform tasks with minimal human involvement. AI automation goes beyond simple rules by understanding context and handling variations.
Example
Automatically routing customer inquiries to the right department based on understanding what the customer is asking about.
Chatbot
A program that can have text or voice conversations with humans. Simple chatbots follow scripts; advanced chatbots use AI to understand natural language and provide dynamic responses.
Example
The chat widget on a website that answers questions about products, hours, or pricing.
Conversational AI
AI technology that enables natural, human-like conversations between computers and people. Uses NLP to understand what people say and generate appropriate responses.
Example
An AI phone agent that can have a natural conversation to book appointments, answer questions, and handle objections.
Deep Learning
A type of machine learning using neural networks with many layers. Enables AI to recognize patterns in unstructured data like text, images, and speech.
Example
The technology that allows AI to understand the meaning of a sentence, not just match keywords.
Fine-Tuning
The process of customizing an AI model for a specific use case by training it on specialized data. Makes general AI models better at specific tasks.
Example
Training a general language model on medical terminology so it can better assist in healthcare settings.
GPT (Generative Pre-trained Transformer)
A type of large language model architecture developed by OpenAI. GPT models are trained to predict and generate text based on patterns in large amounts of data.
Example
ChatGPT, GPT-4, and similar models that can write text, answer questions, and have conversations.
Integration
Connecting AI systems with existing business software so they can share data and trigger actions. Essential for AI agents that need to access or update business systems.
Example
Connecting an AI agent to your calendar so it can check availability and book appointments.
Machine Learning
A type of AI where systems learn patterns from data rather than following explicit programming. The system improves with more data and experience.
Example
An AI that learns to identify spam emails by studying examples of spam and legitimate emails.
Natural Language Processing (NLP)
AI technology that enables computers to understand, interpret, and generate human language. The foundation of all conversational AI.
Example
Understanding that "I want to reschedule my appointment to next Tuesday" means modifying an existing booking.
Neural Network
Computing systems loosely inspired by biological brains, consisting of interconnected nodes that process information. The architecture underlying modern AI.
Example
The mathematical structure that enables AI to recognize patterns in language and images.
On-Premise AI
AI systems that run on hardware located at your business rather than in the cloud. Provides maximum data privacy and control but requires infrastructure investment.
Example
A law firm running its own AI server so client data never leaves the office network.
Prompt
The input or instructions given to an AI model. How you phrase prompts significantly affects the quality and relevance of AI responses.
Example
Asking "Summarize this contract focusing on payment terms" versus just "Summarize this."
Prompt Engineering
The practice of crafting effective prompts to get desired outputs from AI models. A key skill in building AI applications.
Example
Writing system prompts that make an AI assistant stay on topic and follow brand guidelines.
RAG (Retrieval-Augmented Generation)
A technique that combines AI language models with external knowledge sources. AI retrieves relevant information before generating responses, improving accuracy.
Example
An AI that searches your product database before answering customer questions about inventory.
RPA (Robotic Process Automation)
Software that automates repetitive, rule-based tasks by mimicking human actions in digital systems. AI adds intelligence to traditional RPA.
Example
Automatically copying data from emails into a spreadsheet or CRM.
Sentiment Analysis
AI technique that determines the emotional tone of text, whether positive, negative, or neutral. Useful for understanding customer feedback and prioritizing responses.
Example
Identifying that a customer email is frustrated so it can be prioritized for human follow-up.
Token
The basic unit of text that AI models process. Roughly equivalent to word fragments. Important because AI pricing and capabilities are often measured in tokens.
Example
The word "understanding" might be split into tokens like "under" and "standing."
Training Data
The examples and information used to teach an AI model. The quality and relevance of training data significantly impacts AI performance.
Example
Customer service transcripts used to train an AI to handle similar conversations.
Voice AI
AI systems that can understand spoken language and respond with synthesized speech. Enables phone-based AI assistants and voice interfaces.
Example
An AI that answers phone calls, has natural conversations, and handles appointments.
Most Searched AI Terms
The questions business owners ask most often
What is an AI Agent?
An AI agent is software that can autonomously understand requests, make decisions, and take actions without human intervention. Unlike simple chatbots that follow scripts, AI agents can perform complex tasks like booking appointments, processing refunds, or updating multiple systems based on a single customer request. For businesses, AI agents mean automating customer interactions that previously required human staff.
What is an LLM (Large Language Model)?
A Large Language Model (LLM) is an AI system trained on massive amounts of text data to understand and generate human language. Models like GPT-4, Claude, and Llama power modern AI assistants and chatbots. LLMs can understand context, answer questions, write content, and have natural conversations. For businesses, LLMs enable AI customer service, content creation, and document processing.
What is the difference between a chatbot and an AI agent?
Chatbots are conversation interfaces, often following predefined scripts or decision trees. AI agents are autonomous software that can understand intent, access multiple systems, and take actions. A chatbot might answer FAQs; an AI agent can check your calendar, book an appointment, send a confirmation email, and update your CRM, all from one customer request. AI agents are chatbots with capabilities.