When AI Sounds Like a Foreign Language (And How to Order Your Digital Sandwich)
Breaking down the jargon so you can actually use these tools
Growing up in Pittsburgh, we have our own dialect or words for things that make absolutely no sense to anyone else who isn't familiar with how we talk. Instead of saying "you all," we say "yinz." Instead of saying "soda," we say "pop." Over-easy eggs are dippy eggs.
I remember when I was a young teen, vacationing in Virginia Beach with family and some family friends. We stopped at this corner deli sub shop while taking a break from a day at the beach. I kept trying to order a sandwich, but I kept calling it a hoagie instead of a sub. The person behind the counter seemed confused, which was frustrating me because it seemed like we were never on the same page.
How hard should it have been to order a sandwich?
The Real Cost of Confusion: Why Simple Tasks Feel Impossible
I must have said "hoagie" ten times (or what I remember as a million times), and she kept saying "you want a sub." The confusion was mutual. She looked at me like I was speaking another language. I looked at her as if she were being deliberately difficult.
Finally, I pointed to a picture and said, "I want a sandwich with meat, cheese, and heated." That's when she said, "Yes, you want a sub." Young me felt embarrassed that I didn't know the proper term. Current me finds it fascinating that there was such a divide over such a simple thing.
We both wanted the exact same outcome. We just didn't have the shared vocabulary to get there efficiently.
The Problem Isn't the Technology—It's the Translation
But here's the kicker...
Words matter.
Terms matter.
The way you describe something matters.
It's not about being right or wrong—it's about being understood.
When Business Owners and Tech Experts Speak Different Languages
That deli moment taught me something I use every single day now: when people don't understand each other, it's usually not about intelligence or capability. It's about language.
And it's the same with artificial intelligence. Coming from a coding background, I understand some of the terms that are being used to describe certain parts of the AI infrastructure.
But when I'm talking to clients about AI, we're often speaking different languages just like that day at the deli.
They want to "make their business smarter," and I'm thinking about context windows and token limits. They ask, "Can it learn?" and I explain the difference between training data and memory.
Why AI Sounds Complicated (But Doesn't Have to Be)
The real problem isn't that AI is complicated—it's that we're using insider language to describe everyday tools.
Think about it: you don't need to understand internal combustion engines to drive a car. You don't need to know how electricity works to flip a light switch. But somehow, we've convinced ourselves that you need a computer science degree to use AI effectively.
That's nonsense.
What you need is a shared vocabulary. The same way I needed to know that "sub" and "hoagie" meant the same sandwich, you need to know what people mean when they say "LLM," "prompt," or "agent."
Not because the technical details matter for most of what you're trying to do, but because understanding the language helps you ask better questions and get better results.
Someone Who Gets It
– Visual AI Creator: Heather gets it. She started out just as confused by AI jargon as anyone else. "I'm not a traditional technologist," she says, which is exactly why I love following her work. When she first stumbled into AI tools, she found them overwhelming and hard to understand.
But here's what she figured out that most people miss—she doesn't get caught up trying to master every new tool that comes out. Instead, she digs into the nuts and bolts of how to use this stuff better. She'll give you the exact prompt she used, show you her real workflow, and explain it all. No fancy terminology, no showing off—just "here's what worked and here's how you do it."
The Simple Truth About AI Terminology
Here's what nobody tells you about AI terminology: most of it describes things you already understand.
You've been "prompting" people your whole life—it's called giving clear instructions. You understand "memory"—it's just remembering previous conversations. You know what "tools" are—they're the apps and websites that help you get work done.
The only difference is that now we have fancy names for familiar concepts.
Stop Learning AI—Start Speaking Human
This isn't about dumbing down AI—it's about speaking human.
When you strip away the jargon, AI tools become what they actually are: really sophisticated assistants that can help you write, research, organize, and think through problems. They're not magic, they're not going to replace your judgment, and they're definitely not as complicated as the terminology makes them sound.
The goal isn't to become an AI expert. The goal is to become fluent enough in the language that you can order your digital sandwich without pointing at pictures.
Your First Step: Learning to Order Your Digital Sandwich
So here's what I wish someone would talk about for AI terms—the same thing I wish that deli server had told teenage me about regional sandwich names:
We're all talking about the same thing. We just need to agree on what to call it.
What if learning AI wasn't about mastering complex technology, but simply learning to translate between what you want to accomplish and how these tools actually work?
How to Become AI-Fluent in 4 Simple Steps
Your AI Vocabulary Starter Kit:
Save this simple translation guide – bookmark the definitions below for when you encounter AI jargon in articles or conversations
Practice the language – next time you use ChatGPT or Claude, notice you're writing a "prompt" and getting a response from an "LLM"
Ask better questions – instead of "Can AI help my business?" try "Which AI tools handle the specific tasks I need to automate?"
Join the conversation – now that you speak the language, you can participate in AI discussions without feeling lost.
Your AI Terms Translation Guide
Large Language Model (LLM)
What it is: An AI program that's read lots of text, learned how language works, and can now answer questions or write content—like ChatGPT or Claude. Why it matters: These are the "brains" behind most modern chatbots and writing AI tools.
Agent
What it is: An AI that doesn't just answer questions—it can make decisions and take actions on its own. Real-life example: An AI that checks your calendar, finds a free slot, and schedules a meeting automatically.
Model
What it is: The core technology (like GPT-4 or Claude) that powers an AI system. Each model has different strengths. Why it matters: Some models are better at images, some at language, some at reasoning.
Prompt
What it is: The instruction or question you give an AI (like "Write a thank-you email to my boss"). Why it matters: Better prompts get you better answers from AI.
Memory
What it is: The ability of AI to remember previous inputs and use that info in future responses. Real-life example: An agent that remembers your favorite coffee order or your business priorities.
Hallucination
What it is: When AI gives a convincing but made-up answer (not based on real facts). Why it matters: Always double-check important information from AI.
Token
What it is: AI splits sentences into chunks called tokens for processing. About 1,000 tokens equals 750 words. Why it matters: Helps you avoid overloading the AI with too much text at once.
Context Window
What it is: The amount of text an AI can "remember" at once in a single conversation. Real-life example: If your conversation gets very long, earlier messages might be forgotten.
The Real Landing (Call to Action)
Ready to speak AI fluently?
Stop letting jargon intimidate you out of using tools that could transform how you work. These terms aren't barriers—they're just vocabulary words waiting to be learned.
What AI task have you been putting off because the language felt too complicated? Hit reply and tell me—I'll help you translate it into plain English.