近期关于Apple’s AI的讨论持续升温。我们从海量信息中筛选出最具价值的几个要点，供您参考。
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第三，This is exactly the gap that Retrieval-Augmented Generation (RAG) with vector embeddings was built to fill — by matching meaning, not just keywords. In this article, we’ll break down how each approach works, where each one wins, and why production systems increasingly use both together.
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总的来看，Apple’s AI正在经历一个关键的转型期。在这个过程中，保持对行业动态的敏感度和前瞻性思维尤为重要。我们将持续关注并带来更多深度分析。