What AI Terms Should I Know?

Whether you're experimenting with AI tools or helping your organization scale adoption, a shared vocabulary makes everything easier. In the fast-moving world of AI, even basic terms can be confusing—especially when people use them differently across teams, products, or industries.

This post breaks down essential AI vocabulary for the workplace. These definitions are designed to be clear, human-friendly, and practical—so you can confidently lead or participate in AI-related conversations at work.

The Basics: What Is AI, Really?

Artificial Intelligence (AI) refers to machines or software that can perform tasks that typically require human intelligence—like understanding language, recognizing patterns, or making decisions. At work, this shows up as tools that can draft content, answer questions, generate insights, or automate routine tasks.

Prompt vs. Agent Instructions

These two are often confused—but they serve very different purposes.

Prompt = A One-Time Ask

Think of a prompt as a single request you type into an AI tool: “Summarize this email.”“ Write a thank-you note in a friendly tone.”

You're giving the AI a task to complete in the moment, just like asking a colleague to help with something right now.

Agent Instructions = The Job Description

Agent instructions tell the AI how it should behave every time it’s used. They set the long-term role, tone, and rules—like a job description for an AI assistant.

For example: "You are an HR support agent. You respond in a warm, professional tone and escalate compliance issues to HR leadership."

Prompts are one-off asks. Agent instructions guide the AI’s consistent identity and behavior across all tasks.

What Is Prompt Engineering?

Prompt engineering is the practice of crafting a structured prompt to get better results from an AI tool. A good prompt follows a pattern like:

Task, Context, Example, Persona, Format, Tone

For example: “Draft a response email (task) to a customer who reported a bug (context). Here’s the original message (example). Act as a technical support lead (persona). Keep it under 200 words (format), and make it reassuring (tone).”

You don’t need to be an engineer to practice prompt engineering—it’s a skill anyone can build.

Chatbot vs. AI Agent

These are often used interchangeably—but there’s an important difference.

Traditional Chatbot

A traditional chatbot is often rule-based or intent-based, designed to follow defined paths, scripts, or approved response patterns. Think of the little window that says, “How can I help you today?” and then gives you a limited set of responses. It’s great for FAQs or routing requests, but not for handling complex or open-ended tasks.

AI Agent

An AI agent is more advanced. It can follow flexible instructions, pull from data sources, and act like a digital coworker. You can assign it a role, provide instructions, and even teach it how to complete tasks over time. It can use context, instructions, connected data, and tools to adapt its response or take action—much more like a task-focused assistant than a form-filler.

What’s a Core AI Provider?

AI tools are often powered by a foundational engine—and that engine usually comes from a core AI provider like:

• OpenAI (makers of ChatGPT)

• Anthropic (makers of Claude)

• Google DeepMind (Gemini)

• Mistral, Cohere, and others

These companies develop large language models (LLMs)—the underlying AI brains that power everything from chat tools to document assistants.

But here's the twist...

Many software companies (like Notion, Canva, Microsoft, and even HR platforms) don’t build their own AI models. Instead, they use APIs from core AI providers to add AI into their tools. This is called embedding AI—and it’s like adding an AI co-pilot to existing software.

So when someone says, “Our AI is powered by OpenAI,” that means they’re using OpenAI’s engine inside their own product.

Commonly Confused Terms

Term	What It Really Means
AI	Any machine-based intelligence (broad term)
ML (Machine Learning)	A method of building AI systems that learn from data
LLM (Large Language Model)	The AI engine trained on text, powering tools like ChatGPT
Generative AI	AI that creates new content (text, images, code, etc.)
Fine-tuning	Further training or adapting a model on selected examples so it performs better for a specific task, format, domain, or behavior
Embedding	Representing text, images, or other data as numerical vectors so systems can compare meaning and support search or retrieval
Inference	The process of an AI model generating an output (answer, summary, etc.)

Why This Vocabulary Matters

If your team is adopting AI—or even exploring its possibilities—shared language helps:

• Avoid confusion when selecting tools

• Ask smarter questions during demos

• Write better prompts and instructions

• Reduce friction between technical and non-technical teammates

• Build confidence in using AI responsibly and effectively

Final Takeaway

Understanding AI doesn’t require a computer science degree. It just takes the right vocabulary, a few practical examples—and a little curiosity.

If you're part of our AI Accelerator or implementing AI at work, start by using this guide in onboarding or internal training. Or better yet—turn it into a glossary in your internal wiki space.

Use this Prompt: Act as an AI vocabulary coach for the workplace. Review this AI term or concept: [insert term]. Explain it in plain language for a non-technical workplace audience, give one practical work example, clarify what it is not, and identify one common misconception. Return the answer in this format: simple definition, workplace example, what people often confuse it with, why it matters, and one sentence I can use to explain it to a colleague.