Intro to LLMs

We’ve all played around with ChatGPT, Claude, or whatever AI assistant is trending this month.
You type something in, it spits something out, you either nod in approval or shake your head in disbelief.

But under the hood, these large language models (LLMs) aren’t magic.
They’re complex, math-heavy, and — if you understand them just a little better — you can squeeze much more value out of them.

Let’s go beyond the surface.

The Basics: What’s Happening Behind the Scenes

At their core, LLMs are just… really good next-word guessers.

You give them text. They look at it and try to predict the most likely next token (a word or part of a word). Then they do it again. And again. Until you stop them.

This “guessing” isn’t random — it’s based on billions (or trillions) of examples they’ve seen during training.
The magic is in how they represent and connect information:

• Tokenization — Text is broken down into chunks called tokens. “Cat” might be one token, “playground” might be two. The model never truly sees words, only tokens.
• Embeddings — Each token is turned into a vector (a list of numbers) that captures its meaning.
• Transformers — A special architecture that uses something called attention to figure out which parts of your input are important when predicting the next token.
• Parameters — The knobs and dials (weights) learned during training that shape how the model predicts text. GPT-4, for example, has hundreds of billions of these.

You don’t have to understand the calculus behind it — but knowing this helps you see why the model sometimes… hallucinates or drifts off-topic.

Why They Work Well for Some Tasks (and Fail for Others)

LLMs are brilliant at:

• Generating text in a given style.
• Summarizing and rephrasing.
• Filling in gaps in incomplete information.
• Translating between languages or formats.

They struggle with:

• Precise math or logic beyond a few steps (without tools).
• Keeping track of complex, changing rules over long conversations.
• Anything requiring real-time data — unless hooked up to it.
• “Knowing” facts after their training cut-off (unless augmented).

This is because they don’t think or understand like humans.
They don’t “know” things; they recognize patterns.

How to Get Better Results from an LLM

If you treat an LLM like a Google search box, you’ll get average results.
If you treat it like a junior teammate who needs context, you’ll get much better output.

Here’s how:

1. Show, don’t tell
   Give examples of the style or format you want — the model learns instantly from them.
2. Break big tasks into steps
   Ask it to outline first, then fill in details. This keeps it focused.
3. Use the right temperature
   In API calls, a lower temperature = more predictable answers; higher = more creative.
4. Combine LLMs with tools
   The best results come when you use LLMs alongside code, databases, or APIs — letting them fetch facts instead of making them up.

Give context, not just questions
Instead of:

"Write me a marketing email."
Try:
"You are a senior copywriter. Our product is a budgeting app for freelancers. We want a friendly, encouraging tone. Write a 100-word launch email."

The Next Step: Making LLMs Work for Your Use Case

You don’t have to train your own model from scratch.
You can:

• Fine-tune an existing LLM with examples from your domain.
• Use embeddings to let it search your own documents.
• Chain prompts so it works through a process step-by-step.
• Plug it into your systems so it can act, not just talk.

Think of it less as “I’m using ChatGPT” and more as “I’m building a tool that uses a language model under the hood.”

The difference?
In the first case, you’re just a user.
In the second, you’re a creator shaping AI to work exactly the way you want.

Final Thoughts

LLMs aren’t replacing developers, writers, or analysts anytime soon.
But the people who understand how they work — and how to bend them to their will — will run circles around those who just type random prompts and hope for the best.

So next time you fire up ChatGPT or any other LLM, remember:
You’re not just talking to a magic box.
You’re steering one of the most sophisticated text engines ever built.
And with the right approach, it’ll feel a lot more like a partner than a toy.