Habr translation explains how transformer-based LLMs actually work

Habr has published a translation of an article that explains in detail how large language models (LLMs) work. Because the vast majority of modern LLMs are built on the transformer architecture, understanding it gives a picture of the key mechanisms that make these models run, and the article is structured around it.

Why the transformer architecture is the key to understanding LLMs

The transformer has become the foundation for virtually all major language models of recent years, from open research models to commercial products from leading AI labs. At its core is the attention mechanism, which lets the model, while processing each word of a text, take into account its relationships with other words across the whole sequence, not just its nearest neighbors, as earlier recurrent architectures did. This is what drove the qualitative leap in models' ability to understand context that came when the industry moved to transformers.

• The article centers on explaining the transformer architecture as the foundation of modern LLMs
• The author deliberately avoids complex math, aiming at readers new to the topic
• By the author's own admission, a deeper understanding of the subject will still require a mathematical background
• The article is a translation; the original was written in another language

What a reader without math training gets

The author states plainly that the article does not replace a rigorous mathematical treatment, but gives enough for a first introduction to how a transformer turns input text into a meaningful output: through tokenization, the representation of words as vectors (embeddings), and successive layers of attention and fully connected transformations that gradually refine the prediction of the next token. This "ideas without formulas" format is meant for developers and curious readers who want to understand what happens inside an LLM but are not ready to dive straight into the matrix algebra and probability theory underlying the original scientific publications on the topic.

What this means

The appearance of yet another accessible explanation of the transformer architecture reflects the undiminished interest of the Russian-speaking technical audience in how LLMs work under the hood. As language models become part of everyday work tools, demand grows for understanding the principles behind them, not just for knowing how to use them.