AI Fundamentals

Transformer Architecture

The Transformer architecture is the neural network design behind all modern Large Language Models. Its attention mechanism captures relationships in long texts by weighting each word against every other. Since the "Attention Is All You Need" paper (2017), this architecture has transformed the entire AI landscape.

Why does this matter?

The Transformer architecture is why AI understands natural language so well today. For decision-makers, this means: the quality leaps of recent years — from GPT-3 to GPT-4, from simple chatbots to autonomous agents — are all based on this core technology and its advancements.

How IJONIS uses this

We use Transformer-based models as the core building block of our AI solutions — whether as cloud API or local deployment. Our team understands the architecture down to the attention layer, enabling us to optimize models specifically for your use case through adjusted context windows or specialized fine-tuning.

Frequently Asked Questions

Why are Transformers better than previous AI architectures?
Older architectures (RNNs, LSTMs) processed text sequentially — word by word. Transformers process the entire text in parallel and detect relationships across large distances. This enables faster training and better understanding of long documents.
Do I need to understand the Transformer architecture to use AI?
No. As a decision-maker, it is enough to know that Transformers are the foundation of all modern language models. Your implementation partner handles the technical details. What matters more is choosing the right use case and the appropriate model.

Want to learn more?

Find out how we apply this technology for your business.