Six Shocking Secrets: Unpacking the Transformer, Attention, and the Geometry of LLM Intelligence
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Six Shocking Secrets: Unpacking the Transformer, Attention, and the Geometry of LLM Intelligence
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This episode is based on an article written by Alexander Rodrigues Silva of the Semantic SEO blog, which presents an in-depth analysis of the inner workings of Large-Scale Language Models (LLMs), particularly the Transformer engine and its central Attention component. The author shares six surprising discoveries that emerged from a series of interactions with his AI agent, offered as a service called Agent+Semantic. The explanations focus on how words acquire contextual meaning through initial vectors and internal Query, Key, and Value dialogues, showing that meaning is encoded as geometric directions in a multidimensional space. Finally, the text demystifies the concept of machine "learning," comparing it to a mathematical optimization process, like a ball rolling downhill in the cost function.