Use Cases

Attention mechanisms form the backbone of how large language models work. By computing a weight for every token's relevance to every other token in the context, self‑attention lets the model capture long‑range dependencies without the bottlenecks of recurrent networks. Each layer refines these attention weights, so later layers can focus on higher‑level patterns—syntax, semantics, even pragmatic cues—rather than just local word order. In practice, this means the model can weigh "bank" differently in "river bank" versus "bank loan" within the same pass, dynamically reallocating its capacity to whichever parts of the prompt matter most for the next prediction. The result is a scalable architecture (the Transformer) that parallelizes training, handles arbitrarily long contexts, and underpins virtually every state‑of‑the‑art LLM today.