LLMs for Boomers

tl;dr: the Transformer — how attention becomes architecture and how all this reflects God's design.

Last time we talked about attention — how a model figures out which words matter most in a sentence. That was a big step, but attention on its own doesn’t get you very far. It’s like overhearing one line of conversation at a busy dinner table. You catch the words, but without the larger context, you don’t know the full story.

That’s where the Transformer comes in…

”Autobots, roll out!”

Transformers are the framework that make attention usable. By layering attention and other components together, Transformers can handle long passages, keep context, and build meaning step by step.

💡 Why this matters: When we look at how Transformers work, we’re not just seeing clever math. We’re seeing patterns that echo God’s design — the same order we find in our brains, in relationships, and in creation itself. The fact that we can discover these patterns and turn them into technology reflects the creativity He built into us.

Why Attention Alone Isn’t Enough

Attention helps a model connect words, but it doesn’t organize the bigger picture.

Think of watching a Netflix series by skipping to one random episode. You might pick up some characters and a plot point or two, but you won’t understand how everything fits together.

Attention works the same way. It’s good at finding local details, but without a larger structure, it struggles to connect the beginning to the end. That larger structure is what the Transformer provides.

The Transformer, in Plain English

The Transformer is built from repeating blocks of components. Here’s what’s inside each block:

Multi-Head Attention
- Imagine a project team at work. One person is tracking deadlines, another is coding their life away, another is worried about the system design choices, and another one is talking to customers. Each one sees something different, and when they come together, you get the full picture.
Feedforward Layers
- After gathering those perspectives, someone still has to summarize the discussion and make a decision. That’s what the feedforward layer does — it processes the input into something more focused.
Positional Encoding
- Order matters. “The manager praised the employee” is different from “The employee praised the manager.” Positional encoding keeps the order clear, so meaning doesn’t get scrambled.
Residual Connections + Normalization
- These are like spotters at the gym. As the weight (layers) gets heavier, they keep things stable so you don’t drop the bar.

Stack enough of these blocks, and you have a model that can hold onto small details and big themes at the same time.

Walking Through an Example

Take this sentence:
“The coworker who stayed late finished the project.”

Attention links “coworker” ↔ “stayed late.”
One attention head might focus on the action (“finished”), another on the timing (“stayed late”), another on the subject.
Positional encoding keeps the roles straight so we don’t flip who did what.
Feedforward layers refine those insights into the bigger idea: the project was completed because someone worked extra hours.

That’s how the Transformer builds understanding — piece by piece, layer by layer.

Why Transformers Changed Everything

Before Transformers, models were like someone telling you a long story one sentence at a time over the phone. By the time they reached the point, you’d already forgotten how it started. It was slow, linear, and bad at keeping track of context.

Transformers changed that because they:

Look at everything at once — like scrolling the whole group chat instead of reading one text at a time.
Connect details across long passages — like remembering that the inside joke from a couple hours ago explains why everyone is still laughing the next day.
Scale up easily with more data, more layers, and more compute.

That’s why every modern large language model since 2017 is built on this architecture.

The Trade-Offs

Of course, Transformers aren’t perfect:

Expensive: comparing every word to every other word is like trying to review every email in your inbox at the same time.
Resource-heavy: training takes serious hardware, just like running a marathon takes serious conditioning.
Imperfect: even with all that, they can still misinterpret or make things up — like a friend confidently retelling a story but getting the details wrong.

Why This Matters

At one level, Transformers are a clever engineering solution. But look deeper:

The math that powers them was already built into creation — we just uncovered it.
The design echoes the brain, which God designed with far greater complexity.
The order we see in Transformers reflects the order God wove into creation itself.

Colossians 1:16 reminds us: “For in him all things were created: things in heaven and on earth, visible and invisible…”

So when we learn about Transformers, we’re not just learning about AI. We’re seeing a small reflection of how God made an ordered world and gave us the ability to discover and create within it.

Conclusion

Attention gave us the spark.
The Transformer gave us the structure.
Together, they reshaped technology.

But beyond the tech, they remind us that behind human creativity is God’s creativity. The same God who designed galaxies, neurons, and language also gave us the ability to uncover patterns and build tools.

Next time, we’ll look at training: how these models learn, and what that process shows us about how God designed human learning and growth.

LLMs for Boomers - Pt. 3