Large Language Models like GPT-4 and Claude might seem like magic, but at their core, they're doing something surprisingly human-like: paying attention to what matters.
The Power of Context
Think about how you understand language. When you read "The crane flew away", you immediately picture a bird. But if you read "The crane lifted a car", you're thinking of construction equipment. Same word, completely different meaning—and you figured it out by looking at the surrounding context.
This is exactly what attention mechanisms do in AI models. They allow each word to "look around" at other words in a sentence and decide which ones are most relevant for understanding its meaning.
How Attention Actually Works
The math behind attention is elegantly simple. Each word is compared with every other word in the sentence using similarity scores. Words that are more similar get higher weights. Each word's final representation becomes a weighted combination of all words, pulled toward the most relevant ones.
So crane gets pulled toward flew (suggesting a bird) in the first sentence, and toward car (suggesting machinery) in the second.
The Efficiency Problem
Here's where it gets interesting for performance. When generating text word by word, naive attention would recalculate everything from scratch:
Step | Generated Text | Matrix Size |
|---|---|---|
| 1 | I | 1×1 attention matrix |
| 2 | I am | 2×2 attention matrix |
| 3 | I am going | 3×3 attention matrix |
| 4 | I am going to | 4×4 attention matrix |
This gets expensive fast. For a 1000-word response, you'd need a 1000×1000 matrix calculation at the final step.
KV Caching to the Rescue
KV caching solves this by storing previous calculations and only computing attention for new words. Instead of redoing the full attention calculation every time, the model stores the key-value pairs for past tokens. When generating the next word, it only needs to calculate attention for that new word and reuse the previously saved values.
It's like remembering your work instead of starting over each time.
The Real Magic
What makes LLMs remarkable isn't any single breakthrough—it's how stacking many attention layers lets them capture increasingly sophisticated patterns. Simple attention catches basic word relationships, while deeper layers pick up on sarcasm, cultural references, and complex reasoning.
The "magic" of modern AI isn't magic at all. It's just attention, applied systematically at scale. And that might be even more impressive than magic.
Understanding the mechanisms behind AI helps us use these tools more effectively and sets realistic expectations for what they can and cannot do.