LLMs & Transformers: A Beginner’s Guide for Software Engineers

Large Language Models (LLMs) have gone from experimental research projects to everyday tools in just a few years. Whether it’s ChatGPT generating code, GitHub Copilot refactoring your functions, or AI-powered search engines rewriting how developers find answers, the impact of LLMs is already massive.

But as developers, we don’t just want to use AI—we want to understand it. What exactly is happening under the hood when we type a prompt into an AI chatbot? Why did Transformers suddenly replace older AI models? And most importantly, what makes an LLM different from traditional machine learning models?

This post breaks it all down, end to end, in a way that makes sense—even if you’re new to AI.

What Are LLMs and How Do They Work?

At its core, an LLM (Large Language Model) is nothing more than a really advanced text predictor. If you’ve ever used autocomplete while typing a message, you already understand the basic idea. LLMs just take it to a whole new level.

When you type a sentence like “The quick brown fox jumps over the…”, a basic AI model might predict “lazy dog”, because it has seen that phrase before. But an LLM trained on trillions of words doesn’t just predict one word—it can predict entire sentences, paragraphs, or even essays.

Despite how impressive this seems, LLMs don’t actually understand meaning the way humans do. They don’t “know” what a fox or a dog is. Instead, they calculate probabilities based on everything they’ve seen in their training data. If the phrase “lazy dog” has appeared next to “quick brown fox” millions of times, the model assigns it a high probability and generates it as the next text output.

The result? AI-generated content that feels like human writing, even though the model is really just playing a complex statistical game.

Why Transformers Changed Everything

Before Transformers, language models struggled with memory problems. Early AI models, like Recurrent Neural Networks (RNNs) and Long Short-Term Memory (LSTM) networks, could only process text one word at a time. This might sound fine, but it meant they forgot context quickly.

Imagine reading the sentence:

“The developer who fixed the bug was promoted last Friday.”

By the time an RNN got to “promoted,” it might have already forgotten who the sentence was about. That’s because these older models processed words sequentially, meaning they lost track of long-range dependencies.

Transformers solved this problem by changing the way AI models process text. Instead of reading words one at a time, a Transformer analyzes the entire sentence simultaneously. This means it can track relationships between words, no matter how far apart they are.

⚡ How Transformers Work (Without the Math)

To understand why Transformers are so powerful, think about how we read. When you scan an email, you don’t read every word one by one. Instead, you quickly identify the key phrases that matter.

Transformers do the same thing using a technique called self-attention. Rather than treating all words equally, they assign importance scores to words based on context.

Let’s say an AI is reading the sentence:

The cat sat on the mat, but then the dog barked at the cat.

A traditional model might struggle to connect the first “cat” with the second one because they’re far apart. But a Transformer knows they’re related, because self-attention allows it to focus on important words no matter where they appear.

This ability to process entire sequences at once is what makes LLMs like ChatGPT so powerful. It’s why they can generate long, coherent responses instead of forgetting context halfway through.

How LLMs Actually “Read” Text

When we see words, we recognize them instantly. But LLMs don’t “see” words the way we do—they break text into tokens.

For example, the sentence:

This is Unbelivable!

Might be broken down into:

["This", "is", "Un", "believ", "able", "!"]

Notice how “Unbelivable” is split into “Un” + “believ” + “able”? That’s because tokenizing words into smaller chunks helps the model process new words efficiently. If an LLM encounters an unfamiliar word, it can still understand it by breaking it down into known subwords.

This process is called tokenization, and it’s a crucial step in how LLMs handle language. Every prompt you enter is first tokenized, then converted into numerical representations before being processed by the Transformer model.

What Happens When You Type a Prompt?

Now that we understand the basics, let’s walk through what actually happens when you type a question into ChatGPT.

1. Tokenization – Your input is broken down into tokens (small word chunks).
2. Encoding – Each token is converted into a mathematical representation (vector).
3. Self-Attention Processing – The Transformer scans all tokens at once, identifying which words matter most.
4. Prediction & Decoding – The model calculates the most likely next token, then generates a response one token at a time.

This process happens at lightning speed, allowing AI to generate full responses in seconds.

Why Do LLMs Feel So Smart?

At this point, you might be wondering: If LLMs are just predicting text, why do they feel intelligent?

The answer lies in scale.

Modern LLMs are trained on trillions of words, allowing them to learn language structures, grammar, reasoning patterns, and even common knowledge. When you ask a question, the model isn’t “thinking”—it’s simply retrieving the most statistically relevant response based on what it has seen before.

This is why LLMs can:

• Summarize complex documents
• Write code snippets
• Translate between languages
• Even generate creative writing

However, they aren’t perfect—they don’t have real understanding or reasoning, which is why they sometimes generate plausible but incorrect answers (also known as hallucinations).

That brings us to the big question: How does all of this impact developers?

Why This Matters for Developers

Whether you realize it or not, LLMs are already changing how we write code, search for information, and automate workflows. The more you understand how they work, the better positioned you’ll be to use them effectively.

AI-powered coding assistants like GitHub Copilot are getting better at predicting entire code blocks. Search engines like Perplexity AI are rewriting how developers find solutions. And with tools like Hugging Face, you can integrate LLMs directly into your applications.

Final Thoughts: AI Isn’t Replacing You—But It’s Changing the Game

Understanding LLMs isn’t just about keeping up with the latest tech trends—it’s about staying relevant in a world where AI is becoming a core part of software development.

If you’re a developer, the best thing you can do is start experimenting. Play with LLMs. And most importantly, adapt to how AI is reshaping development workflows.

AI won’t replace developers. But developers who leverage AI effectively will replace those who don’t.