You’ve probably heard the buzz about AI and something called LLMs—short for Large Language Models—but what do these terms really mean? And more importantly, how can they help your business? Whether you’re running a construction firm, a healthcare office, a non-profit, or a professional services business, this guide will break it all down in plain English.
Let’s start with the basics. AI is like giving your computer a brain (kind of). It’s a way of teaching machines to perform tasks that usually require human smarts, like recognizing patterns, making decisions, or even chatting with people.
You’ve probably already run into AI without even realizing it. Think of:
Siri or Alexa helping you set reminders.
Netflix or Spotify suggesting your next binge-worthy show or playlist.
Customer service chatbots on websites (those little “How can I help you?” pop-ups).
These sorts of examples have been in use for years - even more than a decade. But we all remember when ChatGPT became all the rage. What was different?
ChatGPT was the first "LLM" —Large Language Model— to come into prominence. LLMs are a specific kind of AI that focuses on words. They’re trained to understand and generate human-like text. Picture a robot that’s read millions (or billions!) of books, articles, and conversations. When you talk to it, it “remembers” patterns and comes up with pretty convincing sentences.
Popular examples include ChatGPT and Google’s Gemini. While they have their limits, these tools are changing how businesses tackle everything from customer service to marketing.
It's helpful to know the basics of how LLMs work, because that informs their best use cases and their limitations:
First, the AI model is fed massive amounts of information—like feeding a library into a super-smart computer. For LLMs, this includes everything from books and news articles to online forums. The more it “reads,” the better it gets at understanding context and meaning.
Next, the model gets to work spotting patterns in the data. For example, it learns that the word “coffee” often goes with “morning” or “breakfast.” This step is called training, and it’s powered by something called deep learning—a way of teaching computers to think like our brains (but much faster).
LLMs don’t read sentences like we do. They break everything into smaller pieces called tokens.
Example: The sentence “AI is amazing” becomes three tokens: ["AI", "is", "amazing"]. Why? Because it’s easier for the model to figure out how those pieces fit together and predict what comes next.
When you type in a question or command, the LLM processes your words, predicts the best response based on what it’s learned, and sends back something that (hopefully) makes sense.
Ask ChatGPT to create some content, like a blog post, and I promise you'll be impressed. So, it's easy to attribute "intelligence" to AI, but the fact is that, at this point, it's really just a very, very good "guesser":
LLMs excel at predicting text based on patterns they’ve learned, but they don’t actually “reason” the way people do. For example, they don’t “think through” problems or make logical decisions in the way a human brain might. Instead, they’re essentially giant calculators for language—using probabilities to guess the most appropriate response.
That said, AI researchers are working hard to introduce and improve reasoning capabilities in newer models. For instance, cutting-edge models are being trained to solve complex problems that require logical reasoning, like multi-step math problems or providing detailed explanations. While we’re not quite at the point where AI can fully mimic human thought, we’re moving closer with every iteration.
LLMs also struggle when there’s ambiguity in a question. For example, if you ask, “What’s the best way to do X?” the model might give a generic answer rather than one tailored to a specific scenario. They’re great at providing surface-level insights but might not always deliver deep, contextualized advice.
Because LLMs rely on the data they’ve been trained on, they’re limited by the quality and diversity of that data. If the training data contains gaps or biases, those flaws can show up in the AI’s responses.
Understanding these limitations helps set realistic expectations and allows businesses to use LLMs effectively without over-relying on them.
AI is incredible, but it’s not perfect. Here are a few things to watch out for:
Biases: AI learns from the data it’s given, which means it can pick up on bad habits if the data isn’t diverse or balanced.
Privacy: Make sure any AI tools you use handle customer data responsibly.
Misinformation: AI doesn’t always get it right, so double-check its responses if accuracy is critical.
The key? Use AI as a helper, not a replacement for human expertise. We're not at the point yet where LLMs or AI in general can be trusted to work autonomously on anything but rote tasks. Always review content generated by AI.
AI and LLMs aren’t just buzzwords—they’re powerful tools that can give your business a serious edge. Whether you’re looking to save time, boost productivity, or improve customer experiences, there’s an AI solution that can help.
If you’re curious about what AI could do for your business, reach out to Verve IT. Let’s chat about how we can help you harness this tech to make your life easier.