Training
multi-head attention
multi-head attention is a core generative-AI concept used across modeling, product, and governance discussions.
Expanded definition
multi-head attention shows up constantly when teams ship LLM features. Practically, it influences how you design prompts, evaluate quality, and reason about failure modes. Teams should document how multi-head attention manifests in their stack—data handling, evaluation, and runtime guardrails—and revisit assumptions as models update.
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