How Large Language Models Work: Understanding Attention and Transformers

If you’ve ever wondered how models like GPT-4, GPT-4o, or GPT-5 actually work, you’ve probably heard the word attention. People say things like “transformers use self-attention to process language” — but what does that mean, really?

This article tried to explain the idea using the Richard Feynman technique: take something complicated, break it down to simple parts, and use everyday analogies so you get a mental model of what’s happening — not just jargon.

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Step 1: Text as Building Blocks

Computers can’t “see” words like we do. So before anything happens:

• A sentence like: “The cat sat on the mat.” gets split into tokens (chunks of text like words or sub-words).
• Each token is turned into a vector — basically a list of numbers that captures its meaning.

Think of these vectors as Lego blocks. Each block has its own shape, and the model will try to fit them together to understand the sentence.

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Step 2: Why We Need Context

Words don’t mean much without context:

• “Bank” could mean river bank or financial bank.
• You decide based on surrounding words: “water” points to the river meaning, while “money” points to finance.

So any good language model must look not just at words in isolation, but also at how they relate to each other.

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Step 3: What Attention Actually Does

Attention is like spotlighting the relevant words in a sentence while ignoring the unhelpful ones.

Imagine you’re reading: “The cat sat on the mat. It was fluffy.”

When you see “It,” your brain automatically lights up “cat,” not “mat.” That’s attention in action.

The model does something similar:

• Each token asks: “Who in this sentence should I pay attention to?”
• Every other token raises its hand: “I might be relevant!”
• The model assigns weights (importance scores).
• Higher weight = stronger connection.

So “It” ends up linked more strongly to “cat” than “mat,” because cats are fluffy far more often than mats.

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Step 4: Queries, Keys, and Values (Made Simple)

Here’s how the model decides who to connect to:

• Every token has three roles:
• Query (Q): What am I looking for?
• Key (K): What can I offer?
• Value (V): What information do I carry?

Think of it like a Q&A session at a conference:

• The token “It” (the pronoun) asks: “I’m looking for the noun I refer to.” (that’s 'It's Query).
• “Cat” says: “I am a noun, I can be described as fluffy, and I was mentioned recently.” (that’s its Key).
• “Mat” says: “I am a noun too, but not usually described as fluffy.” (its Key is weaker).

The Query from “It” matches more strongly with the Key from “Cat” than “Mat.”

The model then pulls the Value (information) from “Cat” into “It.” Result: “It” = cat.

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Step 5: Multi-Head Attention

Now imagine instead of one spotlight, the model has many spotlights.

• One spotlight looks for grammar connections (who is the subject, who is the object).
• Another looks for semantic meaning (fluffy animals vs objects).
• Another tracks long-distance references across sentences.

Each spotlight is a head of attention. Together, they give a rich picture of the sentence.

This is called multi-head attention.

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Step 6: Stacking Layers

Transformers don’t just do attention once. They stack dozens or even hundreds of layers.

• Early layers pick up simple patterns: “dogs” and “cats” are similar.
• Middle layers capture grammar: “the subject of this verb is X.”
• Deep layers capture reasoning: “If X is fluffy, then Y must be an animal.”

By the time you reach the final layer, the model has built a complex web of relationships, ready to predict the next word.

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Step 7: Predicting the Next Word

All of this machinery — tokens, vectors, queries, keys, values, attention heads, and layers — boils down to a simple task:

"Predict the next token."

If the text is: “Paris is the capital of” → the model predicts:

• “France” with high probability.
• “Germany” with lower probability.
• Random words with tiny probabilities.

The magic comes from how good attention is at capturing dependencies, letting the model make these predictions with surprising accuracy.

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Why Attention Was Revolutionary

Before transformers, older models like RNNs and LSTMs struggled:

• They read text one word at a time.
• They easily forgot words that came much earlier.

Attention changed the game because:

1. Any word can connect to any other word — no matter how far apart.
2. The model can process all words in parallel — much faster.
3. The model can learn which words to emphasize, instead of treating all equally.

That’s why the 2017 paper was called “Attention Is All You Need.” It really was.

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Limits of Attention

Even attention has challenges:

• Scaling: For every pair of tokens, the model computes a connection. For long texts, this becomes expensive.
• Memory: Storing these connections (the “KV cache”) eats a lot of GPU memory.
• Lost in the middle: Models sometimes focus more on the beginning and end, ignoring the middle.

That’s why research into efficient attention (like FlashAttention, sparse attention, or linear attention) is ongoing.

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Conclusion

The buzzword attention really boils down to this:

It’s a way for models to focus on the right words at the right time.

This simple mechanism, stacked layer upon layer, is what powers the most advanced AI systems today. It lets them handle long sentences, track meaning across context, and generate text that feels coherent and intelligent.

And once you see it this way, transformers stop being a black box — they’re just really good spotlight operators.

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References

1. Vaswani, A. et al. (2017). Attention Is All You Need. arXiv:1706.03762