"We set out to understand why LLMs were not citing Product Hunt and whether we could change that. The most recent Orbit Awards provided a clean test case, and a new tool called @Gauge made the impact measurable. Gauge tracks LLM visibility across major AI models using a large, search-informed set of prompts, giving us a statistically meaningful way to measure citation rate."
"We focused on AI dictation apps, the first Orbit Awards category, as a controlled test, and aimed to promote AI visibility through a new style of category page. After several targeted iterations, Product Hunt shifted from near zero AI citations to consistent inclusion across multiple models. We are now rolling these changes out across Product Hunt. Product Hunt is becoming part of the AI retrieval layer."
Product Hunt found AI assistants rarely cited Product Hunt in product recommendations despite strong reviews, alternative lists, and structured product information. Product Hunt used Orbit Awards and the Gauge tool to measure LLM citation rates across major AI models with search-informed prompts. A controlled test focused on AI dictation apps and iterative category-page changes increased citations across models. The improvements are being rolled out sitewide. AI visibility is positioned as a new distribution layer, with measurement, terminology alignment, and authority emphasized as key strategies.
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