Question Wording, Not AI Engine Choice, Drives Software Recommendation Results

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A developer running AI-powered software recommendation leaderboards found that changing the wording of a buying question had a far greater impact on results than switching between AI engines. When the same 44 questions were posed to ChatGPT, Gemini, and Perplexity about small-business CRM software, at least 7 of the top 10 recommended products overlapped across all engine pairs. However, when the question was reframed around open-source CRM instead, the top 10 results shared zero products with the small-business version — even using the same engine in the same week. SuiteCRM, for instance, appeared in all 44 open-source phrasings but zero times across 132 small-business answers. The experiment concludes that AI recommendation leaderboards effectively measure a 'phrasing family' rather than an objective market landscape.

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