Trained — weights learned from data by any training algorithm (SGD, Adam, evolutionary search, etc.). The algorithm must be generic — it should work with any model and dataset, not just this specific problem. This encourages creative ideas around data format, tokenization, curriculum learning, and architecture search.
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The tracking system consists of three automated scenarios that work together to provide comprehensive AIO monitoring. The first scenario handles query tracking and brand mentions, automatically sending prompts to ChatGPT and recording which sources appear in responses. The second scenario performs keyword performance analysis, tracking specific topics or phrases relevant to your business and monitoring whether you're gaining or losing visibility. The third scenario focuses on competitor tracking, identifying when competitors appear in AI responses and analyzing their positioning compared to yours.
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Google 将 AppFunctions 类比为 Android 的「模型上下文协议」（MCP），可以简单理解为一个对话标准，帮助第三方的 App 应用和 AI 模型进行对接。
Recommenders and Search Tools，更多细节参见91视频