Last week I had three different conversations with three completely different clients. One was a general contractor who installs roofs and handles remodeling projects across Florida, Georgia, and South Carolina. The second conversation was with two attorneys at a law firm here in South Florida. The third was with the general manager of a healthcare clinic we’ve worked with for several years.
Different industries. Different businesses. Different day-to-day challenges.
I know they are three completely different industries, with different types of clients and daily challenges they dead with, but all three asked me almost the exact same question.
“What exactly is an LLM?” More specifically… “What is the model?”
One of them laughed and said, “I get the language part, because we’re typing questions. But if ChatGPT can analyze images, listen to audio, create videos, and even generate graphics, why do people still call it a language model?”
I said, I know. A lot of people are confused about Ai starting from the simplest description of that “ChatGPT, Gemini or Claude are. It’s a very common question WE get asked. In fact, I think it’s one of the biggest misconceptions surrounding AI today.
The term LLM, or Large Language Model, helped introduce millions of people to artificial intelligence, but the technology has evolved much faster than the name.
Today, most of the AI platforms we use every day aren’t just language models anymore, they’re something much bigger and more complex.
Let me try to Break Down the Name to better explain it
The phrase “Large Language Model” sounds technical, but it’s actually pretty straightforward once you understand each word.
Large – refers to the enormous amount of information the model uses to learn. Including millions and millions of data and text that comes from:
The goal of the Ai isn’t to memorize information the way people often imagine. The goal is to learn patterns. The larger and more diverse the information, the better the model becomes at recognizing relationships between ideas and generating useful responses.
Language – The second word is language.
I guess we could say that this is where the definition started for this Ai models. The first generation of these models learned how we, people, communicate through written language. They learned grammar, context, meaning, writing styles, and how ideas connect to one another. That’s why Ai could answer questions, summarize documents, write emails, translate languages, create marketing content, or explain complex topics in plain English. Everything revolved around text. And for a while, that “LLM” description was accurate.
Model – This is usually where people get stuck. – Most pleased we are speaking with about LLMs they always have a hard time understanding what the hell that’s it means.
The word “model” is very broad and ambiguous so it can take multiple meanings. Some people say it sounds like a database full of answers, but it is not just that.
To explain it in a simple way, I guess I could say the model is a form of software or software system.
More specifically, it’s an incredibly sophisticated software system that has learned patterns from massive amounts of information. This is not any regular kind of software.
How AI Models Differ from Traditional Software
Most Traditional software works because programmers write code and create rules to make it functional. For example, if this happens, do that. If the customer clicks this button, open this page. Every possible situation has to be anticipated ahead of time.
Ai models work differently.
Instead of following thousands of predefined instructions, they learn relationships between ideas. When you ask a question, the model doesn’t go looking for a stored answer.
It creates a new response based on everything it has learned. That’s why AI can explain the same concept differently depending on who’s asking. It can be written in different styles. It can brainstorm ideas that have never been written exactly that way before.
It isn’t copying and pasting. It’s generating answers.
That ability to reason across information and generate something new is what makes these models fundamentally different from traditional software.
But even though it is a “Complex Software System, it still needs hardware to function.
I don’t want to go in detail about all the infrastructure behind Ai, but behind that software are enormous data centers filled with specialized processors, AI chips, networking equipment, and the computing power required to run these systems. The hardware provides the horsepower. The model provides the intelligence. Both are essential.
So Why Are They No Longer Just Language Models?
Here’s where things get interesting. Originally, AI accepted one type of input. And it produced one type of output. “Text and Text”.
That’s no longer the case. Today, most leading AI platforms can work with multiple types of information at the same time. You can upload a picture and ask what’s in it.
What modern AI can now do:
- Record a voice conversation and ask for a summary.
- Upload a PDF filled with charts.
- Analyze a spreadsheet.
- Generate an image.
- Create a presentation.
- Build a website.
- Write computer code.
- Produce videos.
- Generate 3D graphics.
- Have a natural voice conversation.
Instead of understanding only language, these models now understand multiple forms of information. That’s what multimodal means. Multiple ways of receiving information. Multiple ways of creating information. Language is still part of the system. It’s just no longer the whole system.
Why Everyone Still Calls Them LLMs
This is where the terminology becomes a little confusing. People still refer to ChatGPT, Gemini, Claude, Grok, Perplexity, and DeepSeek as LLMs.
Technically, that’s not wrong. The language model is still the foundation. But today those platforms combine language reasoning with vision, speech, image generation, document understanding, coding capabilities, and many other AI models working together.
Calling them LLMs today would be like calling a smartphone “a phone.” Yes, it makes phone calls, but that’s only one of dozens of things it can do.
The name has stayed the same while technology keeps evolving. Things are changing so rapidly that it’s hard to keep up with all the changes and updates. It is overwhelming for most people all the changes that are taking place. The description and semantics are often the last thing that people care about, especially when they don’t understand half of what it all means.
At the end of the day, our clients and all the local businesses that we work with have one common question. What does this mean for my Businesses and what can it do for me and my business?
This is exactly why these conversations have become so common. Every week we’re talking with contractors, service providers, attorneys, Doctors, manufacturers, retailers, and local business owners who are trying to understand where AI fits into their companies.
They’re not asking about Ai because they’re curious about technology. They’re asking because they want to know how it can help them serve their customers better, become more efficient, and stay competitive. Those are the conversations we enjoy having the most.
How Xperience Ai Marketing Solutions Approaches AI
At XMS Ai, we don’t look at Ai as something that replaces people. We look at it as something that helps experienced people do better work. Our marketing strategists use it to research and build stronger campaigns. Our SEO specialists use it to analyze data, identify opportunities, and optimize content for both traditional search engines and AI-powered search. Our paid advertising team uses it to improve Google Ads and paid media campaigns. Our designers use it to develop creative concepts faster. Our developers use it to build websites, applications, automations, and internal tools more efficiently. (Just to mention a few of the functionalities and daily usage.)
Even though we are using Ai across the board, every piece of work is still guided by human experience, creativity, and business judgment. AI helps us move faster. It helps us explore more ideas. It helps us spend less time on repetitive work and more time solving real business problems for our clients.
The Challenge Ahead: Understanding What AI Has Become
The conversations I had last week reminded me of something important. The biggest challenge today for our clients, and also for our agency, as a local business, isn’t learning how to use AI. It’s understanding what AI has already become and where it is going from here.
We’re no longer living in a world where artificial intelligence simply writes text. We’re working with intelligent systems that can see, listen, analyze, create, and communicate across almost every form of digital information. And the pace of that evolution isn’t slowing down, neither are us.
Our commitment at XMS Ai, as one of the best and leading Ai Marketing agencies in Florida, is simple. All our team is committed to continue learning, testing, and integrating these intelligent technologies into the work we do every day, so each one of the local businesses that trust us can benefit from the best of both worlds: powerful AI capabilities and experienced people who know how to use them the right way to help their business grow and continue competing on this new world of Ai.