What is an LLM and why is everyone talking about them now?

An LLM (large language model) is, in plain terms, a program trained on a huge amount of text that has learned to predict which word comes after another. It sounds simple, almost silly. And yet that’s where ChatGPT, Claude, Gemini and the whole wave of tools that write you an email or summarize a contract in two seconds come from.

Think of it as your phone’s autocomplete, but trained on an entire library instead of your last few messages. The difference in scale is so big that the result stops looking like autocomplete and starts looking like conversation.

Why now and not ten years ago? Because three things had to come together at once: huge amounts of available text data, computers powerful enough to process it (the famous GPUs, which until recently only mattered to gamers), and a change in the model’s internal design, the so-called “transformer,” which came out of a Google paper in 2017. Without all three pieces together, this would still be a lab experiment.

One thing people assume that isn’t true: the model doesn’t “know” anything the way a person does. It doesn’t look anything up in a database when you ask it something — it generates the answer word by word based on learned patterns. That’s why it sometimes makes things up with total confidence (what’s known as a hallucination), and why it’s best treated not as an oracle but as a tool whose work needs checking.

For a small business this translates into very concrete uses: answering common customer questions, drafting first versions of texts, summarizing long documents. Nothing like science fiction — just a very fast, slightly absent-minded employee whose work you need to review.

If you’re wondering whether it’s worth looking into for your business, the question I’d ask isn’t “what can AI do,” it’s “which repetitive task eats up the most time every week.” That’s usually where the answer is.