Retrieval-Augmented Surveys

Abstract We introduce retrieval-augmented surveys, a novel survey approach that links respondents’ open-ended priorities to concrete policy proposals in institutional databases, enabling personalized measures of how citizens’ concerns map onto formal agendas. Our pipeline elicits and clarifies each respondent’s top issue concern, summarizes items in a policy database and retrieves semantically relevant candidates (“nearest neighbors”) for inclusion in a respondent-tailored survey. We illustrate the approach with an application to political representation in Congress, evaluating performance across four metrics. First, large majorities of respondents judge the retrieved policy items as mostly or fully addressing their concerns. Second, a hand-coding audit corroborates the alignment of retrieved items with respondent issue stances. Third, support for retrieved items is stable over a short-term panel, with 82% of respondents at the top of the support scale remaining there three weeks later. Fourth, in a tailored conjoint, alignment on the nearest neighbor item has a larger estimated effect on candidate choice than benchmark policies. The framework is modular and portable to other settings, such as rulemaking, municipal agendas, and public budgets, enabling scholars to identify where representation succeeds or fails in the institutional pipeline.