Habr Translation Explains How LLMs Actually Work on Transformer Architecture

Habr published a translation of an article that detailed the principles of large language models (LLM). Since the vast majority of modern LLMs are based on the transformer architecture, understanding it provides insight into the key mechanisms that enable these models to work — and it is around this architecture that the material is built.

Why Transformer Architecture is the Key to Understanding LLM

The transformer became the foundation for virtually all major language models in recent years — from open source research models to commercial products of leading AI labs. At the core of the architecture is the attention mechanism, which allows the model to process each word of text while considering connections with other words in the entire sequence, not just with the nearest neighbors, as in earlier recurrent architectures. It is this that underlay the qualitative leap in the models' ability to understand context, which occurred when the industry transitioned to transformers.

• The material is based on explaining transformer architecture as the foundation of modern LLMs
• The author consciously avoids complex mathematics, aiming for a first introduction to the topic
• For a deep understanding of the topic, according to the author, a mathematical foundation is still required
• The article is a translation — the original was written in another language

What a Reader Without Mathematical Training Gets

The author of the material directly clarifies: the article does not replace a rigorous mathematical analysis, but provides sufficient understanding for first acquaintance with how a transformer turns input text into a meaningful answer on output — through tokenization, representing words as vectors (embeddings), and successive layers of attention and fully connected transformations that gradually refine the prediction of the next token. This format of explanation — "idea without formulas" — is designed for developers and those interested who want to understand what happens inside an LLM, but are not ready to immediately immerse themselves in the matrix algebra and probability theory that underpin the original scientific publications on the subject.

What It Means

The emergence of another accessible explanation of transformer architecture reflects the undiminished interest of the Russian-speaking technical audience in how LLMs work internally: as language models become part of everyday work tools, the demand grows not only for the ability to use them, but also for understanding the principles behind their operation.