Large Language Models Explained Like You're a Business Owner, Not a Data Scientist
People ask me how AI works and they brace for a complicated answer. It's really not that bad.
The AI systems making headlines right now — ChatGPT, Claude, Gemini — they're all built on something called Large Language Models. LLMs. Fancy name, simple idea.
The basic idea
An LLM is a system that got trained on a massive pile of text. Books, websites, articles, code, conversations — basically a huge chunk of everything humans have ever written and put online.
During training, it learns patterns. Not just grammar — it picks up how ideas connect, how problems get solved, how different industries work, how people talk. It builds this giant statistical map of human knowledge.
When you ask it something, it's not Googling the answer. It's generating a response based on everything it's learned. Think of it less like a search engine and more like a colleague who's read everything and can apply what they know to your specific question. Not perfect, but surprisingly useful.
Why this matters for your business
Here's the thing: LLMs are general-purpose. They're not built for one job. The same technology can:
- Write a professional email
- Look at a spreadsheet and find patterns
- Read a contract and pull out the important parts
- Write code to automate a process
- Have a natural conversation with a customer
- Turn raw data into a readable report
That flexibility is what makes AI agents possible. You take this general-purpose brain and point it at specific jobs in your business. The agent uses the LLM to think while following rules and workflows you set up.
What it's good at
- Text — emails, reports, summaries, docs, you name it
- Finding patterns in data
- Following processes consistently, every time
- Working around the clock without getting tired or making Monday-morning mistakes
- Handling volume that would bury a human team
What it's not great at (yet)
- Judgment calls that need deep human context (though it's improving fast)
- Anything physical — it can't turn a wrench
- High-stakes decisions where being wrong really matters (you still want a human in the loop)
- Truly original creative work (it's great at variations, less great at genuine invention)
Bottom line
You don't need to understand how the engine works to drive the truck. You just need to know that the technology is real, it's capable, and it can handle actual work in your business today.
The hard part isn't the AI — it's figuring out where and how to apply it to YOUR specific business. That takes experience. And that's the part we handle.