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Five SQL Metrics for an AI Visibility Dashboard, and the Traps Behind Each

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A DEV Community post explains how to build an AI visibility dashboard from daily LLM answers scored for brand mentions. It notes that the data is noisy because answers are non-deterministic, per-prompt sample sizes are small, engines vary widely, and competitor sets change. The author proposes a shared SQLite view so that all dashboard queries draw from one consistent scoring version. Five metrics are covered, including a 7-day rolling share of answers mentioning the brand and per-engine share of voice across tracked brands. Each metric comes with specific pitfalls, such as averaging daily percentages, gaps in daily data, changes to the competitor set, and average position improving while visibility falls.

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