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In this tutorial, the presenter demonstrates a daily workflow that uses Claude, an AI language model, to automatically analyze underperforming ad hooks and generate higher‑performing replacements within minutes. The process begins by reviewing hook rate and hold rate metrics in Ads Manager each morning, identifying ads with low hook rates (e.g., 20‑30%). The presenter then extracts the current hook text, feeds it along with performance data into a carefully crafted Claude prompt, and receives a ranked list of optimized hooks predicted to improve engagement. By swapping the top‑ranked hook, the presenter reports dramatic improvements—one ad’s hook rate rose from the low 20s to 42% without any manual copywriting effort. The entire cycle—from data retrieval to hook replacement—takes under five minutes and is repeated daily to continuously boost ad performance.

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