Attention is the mechanism inside a transformer that lets each token look at all other tokens in the context and decide how much each one matters for the current computation. It's what enables "understanding" relationships — connecting a pronoun to its antecedent, linking a verb to its subject, relating a question to the relevant part of a document.
Multi-head attention runs this process in parallel many times (each "head" learns different relationship types), then combines the results. This is why transformers handle long-range dependencies so much better than older recurrent models.
The practical implications: the "lost in the middle" problem (models pay more attention to the start and end of context) is a real attention effect. Structured prompts that front-load key information work better partly because of how attention distributes across long inputs.
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