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In 1494, Luca Pacioli described double-entry bookkeeping. Every transaction entered twice. Debits must equal credits. The ledger catches the error.
Five hundred years later, that system remains the decisive advantage for AI-native professional services.
Directly asking LLMs for numerical ratings (like purchase intent on a 1–5 scale) usually produces unrealistic, overly positive, or narrowly distributed answers that don’t match real human data.
This paper introduces Semantic Similarity Rating (SSR)— a simple method that first lets the LLM generate free-text reasoning about a product, then maps that text to a realistic Likert distribution using embedding similarity to anchor statements.
