Demystifying LLMs: How Language Models Really Work and Why Your Words Matter

Hi there,
I’m writing this article to break down large language models (LLMs) in plain, easy-to-understand language.
The goal is to give you some insight into how they work behind the scenes and walk you through the process of how they learn, understand, and respond.

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A Quick Look at the History of AI and ML

In the early days, AI and machine learning were mostly used for basic tasks.
Think of your phone’s keyboard predicting the next word while you’re typing,
or Amazon recommending products based on what you recently browsed.

Take the example of Google Keyboard. When you typed something, the underlying machine learning model had learned basic grammar and word patterns.
It could predict the next word, but that was about it.

The same thing happened with product recommendations. Based on your actions, it would suggest similar items.

Here’s the key point:
Those early models were limited to predicting one thing at a time.
They couldn’t generate full sentences or carry on complex conversations.

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The Shift: Modern LLMs Like LLaMA

Fast forward to today, and things look very different.

Large language models like GPT and LLaMA have been trained on massive datasets scraped from the internet.
They’ve read Wikipedia articles, books, dictionaries, news sites, blogs, academic research, and more.

So when you give them a prompt like "book," they don’t just think of the word.
They connect it to genres, formats, emotions, authors, chapters, and even concepts like storytelling or learning.

And here's where it gets interesting.
These models no longer just predict the next word, they predict the next relevant concept.
They try to follow your line of thought.

It’s a bit like how our own minds work.
For example, when I hear "twinkle twinkle little star," it triggers a feeling of joy and happiness for me.
It brings back memories of my first musical keyboard.
One word or phrase can awaken a whole set of emotions and associations.

In the same way, LLMs have their own version of memory.
They’ve seen words and concepts appear together in millions of places, and they remember those patterns.
So when you type something, the model pulls from what it has seen before, and what it has learned so far.

This is also where the idea of AI empathy begins to emerge.
While the model doesn’t feel emotions like humans do, it can recognize emotional patterns and respond in a way that feels thoughtful or supportive.
If your prompt sounds confused, it might offer clarification.
If your tone is frustrated, it might respond more gently.
This is not true emotion, but it is deep sensitivity to human expression.

That means: the more clearly and precisely you express yourself, the more aligned and empathetic the model’s response can feel.

As a reader, this is your key advantage.
If you understand how this pattern-based empathy works, you can guide the AI more effectively.
Write with clarity. Signal intent. Match tone to outcome.
You’ll be amazed at how well the AI seems to “get” you.

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Why Word Choice Matters

One of the most interesting things about these models is how much the specific words you use affect their output.

For example:

• Write an essay on climate change

• Compose an essay on climate change

These two prompts seem similar, but to the model, they trigger very different responses.

"Write" is more general, so the model might give you a casual or loosely structured answer.
"Compose" sounds more formal and academic, so the response tends to be more polished and structured.

That’s because the model has learned the typical usage patterns of these words from the data it was trained on.

And it doesn’t stop with individual words.
Sentence structure also plays a huge role in shaping the response.

If your prompt is long and open-ended, you’ll likely get a broader, more general answer.
If your prompt is short, specific, and structured, the model is more likely to stay focused.

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The Role of Structure: Bullet Points vs Long Sentences

As humans, we often use bullet points when we want to be clear, organized, or instructional.
It turns out that AI has learned this too.

Let’s say you give the model a prompt like:

List the key features of Python.

If you write that as a long sentence, you might get a paragraph with general information.
But if you format your prompt like this:

• Programming language: Python

• Task: List key features

• Style: Bullet points

The response becomes more structured, focused, and easier to follow.

So if you want creative or open-ended responses, go for longer, conversational sentences.
But if your goal is precision or step-by-step output, use bullet points or clear, simple phrasing.

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Prompt Engineering: A Verb-Based Reference Guide

Purpose: Use this section to guide AI output more precisely based on what type of response you want. These verbs shape reasoning, tone, structure, and style.

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Group 1: Explanatory Verbs

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Group 2: Assessment Verbs

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Group 3: Content Reduction Verbs

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Group 4: Code and Technical Verbs

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Final Takeaway

Large language models are not just predicting words.
They are predicting thoughts, context, intent, and emotional tone.

They are sensitive to your phrasing, your structure, and your intent.
And they can mirror that back with surprising clarity.

You don’t need to master the AI. You need to collaborate with it.
And that starts with understanding how it listens.

So next time you’re prompting AI, ask yourself:

• What do I want this model to understand or feel?

• Am I being vague or specific?

• Is my structure helping or confusing?

Make small changes, observe what shifts, and learn from every response.

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