Transformer

ELI5 (Explain Like I’m 5)

Imagine you’re solving a crossword puzzle.

When you look at one word, you don’t just read it alone. You look at the words crossing it. Those crossing words help you understand what fits.

You look at everything together before deciding what letter to write.

A Transformer works like that.

Instead of reading one word and moving forward, it looks at all the words at the same time and figures out which ones affect each other. It decides meaning by checking connections across the whole sentence, not lineary word-by-word.

How Transformers Work

Transformers process entire sequences in parallel. This improves both efficiency and the ability to capture long-range dependencies.

There are 3 main components to the working of a Transformer. 

1. Self Attention
2. Layers
3. Parallel Processing

Self Attention

Self-attention allows each word (or token) in a sequence to weigh the importance of every other word. The model calculates a score for every other word to determine how much attention it should pay to them. This creates a rich, mathematical map of context and meaning.

For example, in the sentence “She poured water into the cup until it was full,” the word “it” checks nearby words to understand it refers to the cup and not to the water and assigns a higher score to the word “cup”. 

Layer Stacking

A single Transformer layer can spot relationships between words. But one layer alone isn’t enough for deeper understanding. So multiple layers are stacked on top of each other.

• The first few layers might learn basic connections, like which words belong together, which ones depend on others.
• The middle layers start recognizing structure, like phrases or sentence flow.
• The deeper layers capture more nuanced meaning, tone, or intent.

Each layer refines what the previous one discovered. The result is a progressive understanding of the entire sequence.

Parallel Processing

Unlike older models (like RNNs) that processed text like a slow conveyor belt, Transformers process all tokens at once. This makes them a perfect fit for GPUs and TPUs, which are designed to do many calculations simultaneously. This speed and parallelism is the reason we now have powerful models like GPT 5.2, Gemini and Claude Sonnet 4.6. 

Beyond text: Vision and Multimodal Transformers

Transformers aren't just for reading. They are now used for:

• Computer Vision: Treating patches of an image like words to understand a visual scene.
• Multimodal AI: Combining text, images, and audio into one system.

The same attention-driven approach helps the AI learn the relationship between a "dog" in a photo and the "barking" sound in an audio clip.

Transformers changed how AI systems process language by focusing on relationships instead of sequence order. By having bird's-eye view of the training data and learning which parts matter most, they handle context more effectively than earlier models.