AI is everywhere right now and most people have no idea what they’re actually using.
That’s a problem. Not because you need a computer science degree to use these tools — you don’t. But understanding what a large language model actually is will change how you use it, what you trust it with, and where you know to be careful.
So let’s break it down.
What It Is
A large language model — LLM — is software trained to understand and generate human language. It can write, summarise, answer questions, write code, translate, and have a conversation.
The “large” part is literal. We’re talking billions of internal parameters and training on an almost incomprehensible amount of text — books, websites, code, articles. More data and more compute than most people can picture.
Here’s the important thing to understand: these models don’t “know” things the way you do. They don’t have memories or opinions. What they have is an extraordinary ability to predict what should come next — based on patterns learned from all that human writing.
Sounds simple. The results are not.
How It’s Built
There are three stages.
Pre-training. The model reads enormous amounts of text and learns to predict the next word in a sequence. Do this billions of times and something interesting happens — the model starts to develop a real understanding of language, logic, and context.
Fine-tuning. This is where the raw model gets shaped into something useful. Human trainers rate responses for helpfulness and accuracy. This process is called RLHF — Reinforcement Learning from Human Feedback. It’s what turns a language predictor into an assistant you can actually use.
Alignment. Making sure the model behaves the way its creators actually intend. This is an active, ongoing area of research and one of the most important problems in the field.
The Architecture Behind It
You’ll hear the word “transformer” a lot. It refers to a breakthrough paper from Google in 2017 called Attention Is All You Need.
The key innovation was something called self-attention — which lets a model understand the relationship between words in a sentence rather than just reading left to right. That’s why a modern LLM can understand that in “the trophy didn’t fit in the suitcase because it was too big,” the word “it” refers to the trophy. Context. Reference. Nuance.
That was a step change. Everything that came after built on it.
Why They Got So Good So Fast
Three things hit at the same time.
- Scale. Bigger models trained on more data consistently performed better. Researchers found reliable patterns — called scaling laws — that predicted improvement.
- Data. The internet gave them decades of human writing to train on.
- Hardware. GPUs and TPUs made it economically possible to run the calculations required.
The models available today aren’t just incrementally better than five years ago. They’re categorically different.
What They’re Actually Good At
- Writing and editing — from quick emails to long-form content
- Code — writing it, explaining it, debugging it
- Research and summarisation — feed it a long document, get the key points back
- Analysis — breaking down arguments, evaluating options, structured thinking
- Creative work — brainstorming, drafts, story development
I’ve used these tools across engineering documentation, design briefs, and writing projects. The time savings are real.
Where They Fall Down
Be honest about this stuff.
They hallucinate. They will sometimes confidently give you information that is completely wrong. Always verify anything important from an independent source.
They have a knowledge cutoff. Most models don’t know about events after a certain date unless they have web access built in.
They have reasoning limits. Complex maths and formal logic can trip them up even when they look like they’re handling it fine.
They carry bias. Trained on human-generated text means trained on human biases. That’s an active problem being worked on.
Context windows are finite. Every model has a limit on how much text it can hold in “memory” at once. Exceed it and it starts forgetting the earlier part of your conversation.
The Models Worth Knowing
GPT-4o (OpenAI) — The engine behind ChatGPT. Best all-rounder, particularly strong at coding.
Claude 3.7 (Anthropic) — Best for writing and long documents. More careful and consistent than the others.
Gemini 1.5 Pro (Google) — Largest context window in the game. Built for processing very long documents.
Llama 3 (Meta) — Open source. Run it locally. Best option when privacy matters.
We’ll go deep on each of these in upcoming posts.
Should You Be Using One?
Yes. Almost certainly.
They’re not magic and they’re not infallible. But as tools for thinking, writing, building, and working — they represent a real change in what’s possible.
The people who get the most out of them aren’t the ones who treat them as oracles. They’re the ones who understand what they’re good at, where they fall short, and how to work with them intelligently.
That’s what we’re here to figure out.
