Key AI Terms You Should Know (Without the Jargon)

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.

Chatbot

A chatbot is usually a rule-based tool designed to follow simple scripts. 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 learns from context and adapts—much more like a teammate 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	Training an AI model further on your own data to specialize it
Embedding	Representing text or data in a format AI can understand and search
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.